System and method for generating a wide-field OCT image of a portion of a sample

ABSTRACT

Various embodiments of systems and methods are described herein for obtaining wide field OCT images and other types of image data from at least one portion of a sample. Various embodiments of systems, and methods are described herein for assessing a degree of differentiation for a second region of an OCT image of a tissue sample with respect to another first. Various embodiments of a sample container are also described herein for containing a tissue sample and maintaining the tissue sample in a defined orientation during imaging or handling.

FIELD

The various embodiments described herein generally relate to a systemand method for obtaining Wide field OCT images.

BACKGROUND

Optical Coherence Tomography (OCT) imaging uses near-infrared light toproduce high-resolution images of various objects such as, but notlimited to tissue, for example. When OCT imaging is used on tissue, itis analogous to high-frequency ultrasound, except that the opticalinterferometry of OCT imaging is used for depth ranging rather than echotiming. OCT imaging is rapid, non-contact, non-invasive, and capable ofgenerating 2D and 3D images at high resolution (˜10 μm).

Current methods to measure margin width during lumpectomy procedureshave been reported to result in reoperation rates of up to 20 to 60%,representing a significant and unmet need for improved marginassessment. High reoperation rates present both increased treatment riskto patients and an increased burden on healthcare systems. In the USAalone, over 150,000 lumpectomies are performed per year at an averagecost of between $11,000 and $19,000 USD per procedure. Assuming anaverage repeat operation rate of 25%, potentially preventable repeatsurgeries represent an approximate cost to the US healthcare system of$500M (USD) annually.

SUMMARY OF VARIOUS EMBODIMENTS

In a broad aspect, at least one embodiment described herein provides animaging system for generating optical coherence tomography (OCT) imagesof a sample. The imaging system comprises a scanning area for receivingthe sample; a scanning assembly disposed within the scanning area, thescanning assembly configured to acquire raw OCT data of at least aportion of the sample from at least two independent directions; and aprocessing module in electrical communication with the scanning assemblyand operable to determine OCT imaging parameters for the raw OCT data,to control the scanning assembly according to the OCT imaging parametersto acquire the raw OCT data of at least the portion of the sample and togenerate one or more corresponding OCT images.

In another broad aspect, at least one embodiment described hereinprovides a method for generating a wide field OCT image of a portion ofa sample. The method comprises creating a surface map of the sample;acquiring raw OCT data of the portion of the sample based on the surfacemap; generating a plurality of OCT images from the raw OCT data; andcombining two or more OCT images of the plurality of OCT images tocreate the wide field OCT image.

In another aspect, at least one embodiment described herein provides asystem for generating a wide field OCT image of a portion of a sample.The system comprises an input port for receiving raw OCT data; aprocessing module configured to conduct the methods for generating awide field OCT image of a portion of a sample in accordance with one ormore of the embodiments described herein; and an output port to providethe wide-field OCT images to one of a user, a storage device and anothercomputing device.

In another aspect, at least one embodiment described herein provides acomputer-readable medium storing computer-executable instructions thatcause a processor to perform one or more of the methods for generating awide field OCT image as described herein.

In another broad aspect, at least one embodiment described hereinprovides a method for generating a wide field OCT image of a portion ofa sample. The method comprises receiving a set of OCT imagescorresponding to the portion of the sample, the set of OCT imagesincluding a first OCT image and a neighbouring OCT image; aligning thefirst OCT image with the neighbouring OCT image; and overlaying aportion of the first OCT image over a portion of the neighbouring OCTimage to create the wide field OCT image.

In another broad aspect, at least one embodiment described hereinprovides a method of assessing a degree of differentiation for a secondregion of an OCT image of a tissue sample with respect to a firstregion. The method comprises receiving OCT image data for the OCT image;identifying a boundary in the OCT image to identify the second region inthe OCT image, the second region being disposed below the boundary;identifying a first set of OCT image data corresponding to the firstregion and a second set of OCT image data corresponding to the secondregion; generating a first optical dataset based on measurements of atleast one first optical characteristic for the first set of OCT imagedata; generating a second optical dataset based on measurements of theat least one first optical characteristic for the second set of OCTimage data; and comparing the first optical dataset and the secondoptical dataset to identify a degree of difference between the firstregion and the second region, wherein a high degree of differenceindicates that the first region and the second region comprise differenttissue types.

In another broad aspect, at least one embodiment described hereinprovides a system for assessing a degree of differentiation for a secondregion of an OCT image of a tissue sample with respect to a firstregion. The system comprises a data interface configured to receive OCTimage data for the OCT image; a user interface configured to receive atleast one input from a user and a display at least one output to theuser; and a processing module coupled to the data interface and the userinterface. The processing module coupled to the data interface and theuser interface, the processing module being configured to identify aboundary in the OCT image to identify the second region in the OCTimage, the second region being disposed below the boundary, to identifya first set of OCT image data corresponding to the first region and asecond set of OCT image data corresponding to the second region, togenerate a first optical dataset based on measurements of at least onefirst optical characteristic for the first set of OCT image data; togenerate a second optical dataset based on measurements of the at leastone first optical characteristic for the second set of OCT image data;and to compare the first optical dataset and the second optical datasetto identify a degree of difference between the first region and thesecond region, wherein a high degree of difference indicates that thefirst and second region comprise different tissue types

In another broad aspect, at least one embodiment described hereinprovides a computer-readable medium storing computer-executableinstructions that cause a processor to perform a method of assessing adegree of differentiation for a second region of an OCT image of atissue sample with respect to a first region, the method comprisingreceiving OCT image data for the OCT image; identifying a boundary inthe OCT image to identify the second region in the OCT image, the secondregion being disposed below the boundary; identifying a first set of OCTimage data corresponding to the first region and a second set of OCTimage data corresponding to the second region; generating a firstoptical dataset based on measurements of at least one first opticalcharacteristic for the first set of OCT image data; generating a secondoptical dataset based on measurements of the at least one first opticalcharacteristic for the second set of OCT image data; and comparing thefirst optical dataset and the second optical dataset to identify adegree of difference between the first region and the second region,wherein a high degree of difference indicates that the first and secondregion comprise different tissue types.

In another broad aspect, at least one embodiment described hereinprovides a sample container for containing a tissue sample andmaintaining the tissue sample in a defined orientation. The containercomprises an interface sleeve having one or more side walls which extendlongitudinally between lower and upper open ends to define an interiorvolume for receiving the tissue sample, the one or more side wallscollectively including a plurality of spaced apart retentive membersdistributed longitudinally between the lower and upper ends; a bottomsample support having an upper surface for supporting the tissue sample,the bottom sample support being releasably engageable with the interfacesleeve for selectively closing the lower end; and a top sample supporthaving a lower surface for supporting the tissue sample, the top samplesupport being sized to be received in the interior volume at a pluralityof longitudinal positions corresponding to the plurality of spaced apartretentive members between the lower and upper ends of the interfacesleeve, the top sample support including one or more retentive membersthat are releasably engageable with the retentive members of theinterface sleeve to selectively lock the top sample support at aselected one of the plurality of longitudinal positions and hold thetissue sample firmly between the lower surface of the top sample supportand the upper surface of the bottom sample support.

In another broad aspect, at least one embodiment described hereinprovides a sample container for containing a tissue sample andmaintaining the tissue sample in a defined orientation. The containercomprises an interface sleeve having one or more side walls which extendlongitudinally between lower and upper open ends to define an interiorvolume for receiving the tissue sample, a bottom sample supportconfigured to selectively close the lower end; and a top sample supportselectively lockable at a plurality of longitudinal positions betweenthe lower and upper ends to firmly hold a tissue sample in the interiorvolume between the bottom sample support and the top sample support.

DETAILED DESCRIPTION OF DRAWINGS

For a better understanding of the various embodiments described herein,and to show more clearly how these various embodiments may be carriedinto effect, reference will be made, by way of example, to theaccompanying drawings which show at least one example embodiment, and inwhich:

FIG. 1 shows an illustration of how an imaging system can be used in anoperating room environment to assess resected tissue sample;

FIG. 2 shows a flowchart of an example embodiment of a method for usingthe imaging system to obtain wide field Optical Coherence Tomography(OCT) images of a sample;

FIG. 3A is a perspective view of an example embodiment of an imagingsystem;

FIG. 3B is a magnified view showing sterile controls of the imagingsystem;

FIG. 3C is a front view of the imaging system;

FIG. 3D is a magnified view of a wheel lock of the imaging system;

FIG. 3E is a rear perspective view of the imaging system;

FIG. 4A is a front view of an example embodiment of a scanning assemblythat may be used in the imaging system of FIGS. 3A to 3E for scanning asample;

FIG. 4B is a rear view of the scanning assembly of FIG. 4A;

FIG. 4C is a perspective view of a trans-rotational mechanism and asupport area in the scanning assembly of FIG. 4A;

FIG. 4D is a perspective view of a container adapter for the scanningassembly of FIG. 4A;

FIG. 4E is a cross-sectional exploded view of a container support andthe container adapter of FIG. 4D for the scanning assembly of FIG. 4A;

FIG. 4F is a front-side perspective view of a tilt mechanism and ascanning head for the scanning assembly of FIG. 4A;

FIG. 4G is a rear perspective view of the tilt mechanism and thescanning head of FIG. 4F;

FIG. 4H is a perspective view of a home device for the scanning assemblyof FIG. 4A;

FIG. 4I is a rear perspective view of the home device of FIG. 4H;

FIG. 4J a front-side perspective view of the tilt mechanism and thescanning head of FIG. 4F coupled of the home device of FIG. 4G;

FIG. 4K is a rear perspective view of FIG. 4J;

FIG. 4L is a perspective view of a first translation mechanism with atilt mechanism and a scanning head for the scanning assembly of FIG. 4A;

FIG. 4M is a perspective view of the first translation mechanism of FIG.4L;

FIG. 4N is a partial front view of the first translation mechanism ofFIG. 4L illustrating components below a carriage plate;

FIG. 4O is a perspective view of a second translation mechanism for thescanning assembly of FIG. 4A;

FIGS. 4P and 4Q are different views of a horizontal encoder for thesecond translation mechanism of FIG. 4O;

FIG. 4R is a perspective view of support brackets for the scanningassembly of FIG. 4A;

FIG. 4S is a perspective view of the scanning assembly of FIG. 4A inaccordance with an example embodiment;

FIG. 5 shows a block diagram of an example embodiment of hardwarecomponents that may be used with the imaging system;

FIGS. 6A and 6B show an example prototype of an imaging system forgenerating wide field OCT image data;

FIG. 6C shows an example schematic of a Spectral-Domain OCT system;

FIG. 7A shows a flowchart of an example embodiment of a wide field OCTimaging method;

FIG. 7B shows a flowchart of an example embodiment of a wide fieldacquisition method;

FIG. 7C shows a flowchart of an example embodiment of a surfacedetection method for OCT images;

FIG. 7D shows a flowchart of an example embodiment of a verticalstitching method;

FIG. 7E shows a flowchart of an example embodiment of a horizontalstitching method;

FIG. 7F shows a flowchart of an example embodiment of a method forminimizing saturation artifacts;

FIG. 8A shows a schematic of an optical path for a lens in accordancewith an example embodiment;

FIG. 8B shows a schematic of an optical path for another lens inaccordance with an example embodiment;

FIG. 8C shows a schematic of a scanning head capturing an OCT image inaccordance with an example embodiment;

FIG. 8D shows a series of OCT images for a sample surface in accordancewith an example embodiment;

FIG. 8E shows a series of OCT images for another sample surface inaccordance with an example embodiment;

FIG. 8F shows a series of OCT image for yet another sample surface inaccordance with an example embodiment;

FIGS. 9A to 9C show an example of how a series of OCT images may becombined for creating a wide field OCT image;

FIGS. 10A to 10C show an example combination of another series of OCTimages for creating a wide field OCT image;

FIGS. 10D to 10F show an example combination of the wide field OCT imageof FIG. 9C and the wide field OCT image of FIG. 10C for creating yetanother wide field OCT image;

FIGS. 11A to 11E show various overlaid OCT images with different offsetdistances in accordance with an example embodiment;

FIG. 11F is a plot of the entropy associated with the overlaid OCTimages of FIGS. 11A to 11E;

FIG. 12A shows an image of a histopathology sample of a rat ovary tumorthat is derived from a human breast cancer cell line (MT-1) xenograft;

FIG. 12B shows a reconstructed OCT image of the rat ovary tumor sampleof FIG. 12A;

FIG. 12C shows an image of another histopathology sample of a rat ovarytumor that is derived from a human breast cancer cell line (MT-1)xenograft;

FIG. 12D shows a reconstructed OCT image of the rat ovary tumor sampleof FIG. 12C;

FIG. 13A shows a white light image of a lumpectomy specimen illustratinga B-scan path;

FIG. 13B shows a reconstructed OCT image of the lumpectomy specimenshown in FIG. 13A over the B-scan path;

FIG. 14A shows a white light image of a lumpectomy specimen illustratinga B-scan path;

FIG. 14B shows a reconstructed OCT image of the lumpectomy specimenshown in FIG. 14A over the B-scan path;

FIG. 15A is an example embodiment of a reconstructed OCT image with theborders of regions of interest identified;

FIG. 15B is an example embodiment of a reconstructed OCT image with aboundary distinguishing two regions identified;

FIG. 15C is an example embodiment of a reconstructed OCT image with amask that can be used to show actionable boundaries and non-actionableboundaries;

FIG. 16 is a flowchart of an example embodiment of a tissue assessmentmethod that may be used by a tissue imaging system or device todetermine the degree of differentiation for different regions of atissue sample;

FIG. 17 is flowchart of an example embodiment of a boundaryidentification method that may be used with the tissue assessment methodof FIG. 16;

FIG. 18A is an example embodiment of a reconstructed OCT image for aportion of an excised tissue sample;

FIGS. 18B to 18D show examples of images indicating the borders ofregions of interest for the reconstructed OCT image of FIG. 18A;

FIGS. 18E to 18G show examples of the OCT image of FIG. 18A along withvarious masks highlighting regions of high attenuation;

FIG. 19A shows an example of a plurality of B-scans for a reconstructedOCT image of an excised tissue sample;

FIG. 19B shows an example of an image indicating the borders for regionsof interest for the plurality of B-scans in FIG. 19A;

FIG. 19C shows a plurality of flattened B-scan images corresponding tothe plurality of B-scans shown in FIG. 19A;

FIG. 19D shows an example of an image indicating the borders for regionsof interest for the plurality of flattened B-scan images of FIG. 19C;

FIG. 19E shows an example image of one of the plurality of B-scans ofthe FIG. 19A with a composite border for regions of interest;

FIG. 19F shows an example of an image indicating the composite borderfor regions of interest for the B-scan image in FIG. 19E;

FIG. 20A is an example image of a flattened window of a B-scan image forwhich a boundary has been accepted;

FIG. 20B shows an example of a plot of signal intensity for an A-scan ofthe B-scan image shown in FIG. 20A;

FIG. 20C shows a portion of the plot of signal intensity of FIG. 20Bcorresponding to a first region;

FIG. 20D shows an example of a normalized plot of signal intensitycorresponding to the portion of the plot of signal intensity shown inFIG. 20C;

FIG. 20E shows a portion of the plot of signal intensity of FIG. 20Bcorresponding to a second region;

FIG. 20F shows an example of a normalized plot of signal intensitycorresponding to the portion of the plot of signal intensity shown inFIG. 20E;

FIG. 20G shows an example plot of the measured attenuation and texturefor two regions of the flattened B-scan window shown in FIG. 20A;

FIG. 21A shows an example plot of an output of an optimization methodfor the two regions of an OCT image of FIG. 20G;

FIG. 21B shows an example plot of another optimization method output forthe two regions of an OCT image of FIG. 20G;

FIG. 22A is another example of an OCT image with a boundarydistinguishing two regions;

FIG. 22B is an example of a flattened window of the OCT image of FIG.22A for which the boundary was accepted;

FIG. 22C shows an example plot of the measured attenuation and texturein two regions of the flattened window of FIG. 22B;

FIG. 22D shows an example plot of an output of an optimization methodfor the two regions in the flattened OCT image of FIG. 22B;

FIG. 22E shows an example plot of an output of another optimizationmethod for the two regions in the flattened OCT image of FIG. 22B;

FIG. 22F shows the OCT image of FIG. 22A with a mask;

FIG. 23A shows a perspective view of a sample container for containing atissue sample, in accordance with at least one embodiment;

FIG. 23B shows a perspective view of a bottom sample support and atissue sample, in accordance with at least one embodiment;

FIG. 23C shows a cross-sectional view taken along line 23C-23C in FIG.23B;

FIG. 23D shows a perspective view of a top sample support, in accordancewith at least one embodiment;

FIG. 23E shows a cross-sectional view taken along line 23E-23E in FIG.23D, in accordance with another embodiment;

FIGS. 23F and 23G show front and rear perspective views of an interfacesleeve, in accordance with at least one embodiment;

FIG. 24A shows a tissue sample placed on a bottom sample support, inaccordance with at least one embodiment;

FIG. 24B shows a top sample support and a bottom sample support insertedinto an interface sleeve, in accordance with at least one embodiment;

FIG. 24C shows a cross-sectional view taken along line 24C-24C in FIG.24B;

FIG. 24D shows a cross-sectional view taken along line 24C-24C in FIG.24B, after the top sample support has been further inserted into theinterface sleeve;

FIG. 24E shows a portion of the upper end of an interface sleeve, inaccordance with at least one embodiment;

FIG. 25A shows a perspective view of a sample container, in accordancewith another embodiment;

FIG. 25B shows a cross-sectional perspective view taken along line25B-25B in FIG. 25A;

FIG. 25C shows a cross-section view of the container of FIG. 25A with atop sample support, a bottom sample support, and a bottom lid engagedwith an interface sleeve, in accordance with at least one embodiment;

FIG. 26A shows a perspective view of a sample container in accordancewith another embodiment;

FIG. 26B shows a cross-sectional view taken along line 26B-26B in FIG.26A;

FIG. 26C shows a perspective view of a top or bottom sample support, inaccordance with at least one embodiment;

FIG. 26D shows a cross-sectional perspective view of the container ofFIG. 26A; and

FIG. 26E shows a perspective view of a top or bottom sample support inaccordance with another embodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Various apparatuses or processes will be described below to provide anexample of an embodiment of the claimed subject matter. No embodimentdescribed below limits any claimed subject matter and any claimedsubject matter may cover processes or apparatuses that differ from thosedescribed below. The claimed subject matter is not necessarily limitedto apparatuses or processes having all of the features of any oneapparatus or process described below or to features common to multipleor all of the apparatuses or processes described below. It is possiblethat an apparatus or process described below is not an embodiment of anyclaimed subject matter. Any subject matter disclosed in an apparatus orprocess described below that is not claimed in this document may be thesubject matter of another protective instrument, for example, acontinuing patent application, and the applicants, inventors or ownersdo not intend to abandon, disclaim or dedicate to the public any suchinvention by its disclosure in this document.

Furthermore, it will be appreciated that for simplicity and clarity ofillustration, where considered appropriate, reference numerals may berepeated among the figures to indicate corresponding or analogouselements. In addition, numerous specific details are set forth in orderto provide a thorough understanding of the embodiments described herein.However, it will be understood by those of ordinary skill in the artthat the embodiments described herein may be practiced without thesespecific details. In other instances, well-known methods, procedures andcomponents have not been described in detail so as not to obscure theembodiments described herein. Also, the description is not to beconsidered as limiting the scope of the embodiments described herein.

It should also be noted that the terms “coupled” or coupling as usedherein can have several different meanings depending on the context inwhich these terms are used. For example, the terms coupled or couplingcan have a mechanical, electrical or optical connotation. For example,depending on the context, the terms coupled or coupling may indicatethat two elements or devices can be physically, electrically oroptically connected to one another or connected to one another throughone or more intermediate elements or devices via a physical, electricalor optical element such as, but not limited to a wire, fiber optic cableor waveguide, for example.

It should be noted that terms of degree such as “substantially”, “about”and “approximately” as used herein mean a reasonable amount of deviationof the modified term such that the end result is not significantlychanged. These terms of degree should be construed as including adeviation of up to ±10% of the modified term if this deviation would notnegate the meaning of the term it modifies.

Furthermore, the recitation of numerical ranges by endpoints hereinincludes all numbers and fractions subsumed within that range (e.g. 1 to5 includes 1, 1.5, 2, 2.75, 3, 3.90, 4, and 5). It is also to beunderstood that all numbers and fractions thereof are presumed to bemodified by the term “about.” The term “about” means up to ±10% of thenumber to which reference is being made.

In the following passages, different aspects of the embodiments aredefined in more detail. Each aspect so defined may be combined with anyother aspect or aspects unless clearly indicated to the contrary. Inparticular, any feature indicated as being preferred or advantageous maybe combined with at least one other feature or features indicated asbeing preferred or advantageous.

Described herein are various example embodiments of a system and methodthat can be used to obtain wide field OCT images. Wide field OCT imaginghas various applications such as, but not limited to, scanning of atissue surface that is larger than the viewable area of a typical OCTsystem and scanning of tissue samples that demand interrogation of thetissue characteristics at depths exceeding the typical penetration depthof a typical OCT system. The imaging window of a typical OCT system istypically a 10 mm×3 mm (W×H) area and the maximum penetration depth oftissue is approximately 2 mm. However, a typical tissue sample has asurface area of approximately 200 cm². Furthermore, the surface of thetissue may be irregular.

An example application of an imaging system will now be described withsimultaneous reference to FIGS. 1 and 2.

FIG. 1 is an illustration of how an imaging system 10 can be used in anoperating room environment to assess resected tissue samples. An exampleof the imaging system 10 may be a Margin Assessment Machine (a MAM).FIG. 2 is a flowchart of an example embodiment of a method 12 for usingthe imaging system 10 to obtain wide field OCT images of a sample. Itshould be noted that the flowchart illustrates only one exampleembodiment for the method 12 and there can be other embodiments in whichdifferent actions may be included or some actions may be removeddepending on the particular application of the imaging system 10 (thisalso applies to the other flowcharts and block diagrams shown anddescribed herein).

At 8 of FIG. 1, a specimen of excised tissue containing the tumor isresected. At 14, the tissue specimen can be placed into a consumable 42,which can also be called a container or sample container. The container42 can be used to maintain the excised tissue in a particularorientation, as will be described with reference to FIGS. 23A to 26E.

At 16, the container 42 is placed in a chamber 40 of the imaging system10 for imaging purposes. The container 42 can be used for a variety ofpurposes, including loading a sample into the imaging system 10,securing the sample during various types of imaging and scanningincluding OCT scanning, and transporting the sample through the clinicalprocess. The sample may be a tissue specimen or other types of materialrequiring OCT imaging.

After the container 42 is placed inside the chamber 40, the imagingsystem 10 can then create wide field OCT images of the tissue specimen,such as at 20. For example, at 20A as shown in FIG. 1, the tissuespecimen may be scanned by the imaging system 10 while inside thechamber 40 and then, at 20B as shown in FIG. 1, the imaging system 10may generate a margin assessment width map or other image informationabout the tissue specimen.

In some embodiments, the imaging system 10 may operate based on inputparameters provided by the user, such as a medical practitioner (e.g., asurgeon). Prior to generating images of the tissue specimen, the imagingsystem 10 may first receive input parameters from the user to specifyvarious parameters for the OCT image data to be generated by the imagingsystem 10. The input parameters may include at least one of selectingregions of interest, selecting a scan density, and selecting a timeconstraint for three-dimensional (3D) OCT imaging of the tissuespecimen.

Examples of various methods associated with creating the wide field OCTimages of the sample according to the teachings herein will be describedwith reference to FIGS. 7A to 7F. Briefly, the methods of creating thewide field OCT images may involve creating a surface map trajectory forthe sample, as will be described with reference to FIG. 7A. The surfacemap trajectory, and possibly other input data from the user, may be usedto determine various positions for a scanning head of the scanningassembly in the imaging system 10 in order to generate desired images ofthe sample. For instance, the scanning head may be used for collectingraw OCT data of the sample, such as regions of interest as indicated bythe input parameters provided by the user. As will be described withrespect to FIG. 4A, the scanning head can be adjusted in variousdirections, such as each of the x, y and z directions in a Cartesiancoordinate system, to obtain or capture the raw OCT data.

To create the wide field OCT images of the sample, the imaging system 10can apply various stitching (i.e. combination or merging) methods to theOCT image data. The stitching methods may include horizontal stitchingand vertical stitching. Vertical stitching may be needed depending on avariability of a height of the surface of the portion of the tissuesample being imaged, as will be generally described with reference toFIGS. 8E and 8F. For example, images associated with an object having anon-uniform surface may require vertical stitching due to the variationsin height across its surface (such as the example shown in FIG. 8F).Alternatively, images associated with an object with a fairly uniformsurface may not require vertical stitching (such as the example shown inFIG. 8E). Example embodiments of stitching methods will be describedwith reference to FIGS. 7D and 7E.

In some embodiments, the imaging system 10 may apply other imageprocessing techniques to improve image quality of the wide field OCTimages. These image processing techniques may be used, but not required,if aberrations typically inherent to OCT are present in the created OCTimages. An example image processing technique will be described withreference to FIG. 7F.

At 24, the imaging system 10 determines whether the container 42 shouldbe flipped (e.g. inverted) so that the imaging system 10 can captureadditional OCT image data from another portion of the tissue specimen.For example, the container 42 can be flipped to switch hemispheres inorder to image the entire surface of the sample. Whether or not thecontainer 42 is flipped may depend on the input parameters provided bythe user and/or default settings of the imaging system 10. If theimaging system 10 determines that the container 42 is to be flipped, theimaging system 10 allows for the operator of the imaging system 10 toaccess the container 42 and manually flip the container. The imagingsystem 10 can then repeat the creation of the wide field OCT images (at20) for the flipped tissue specimen. If the imaging system 10 determinesthat no further OCT image data is needed, the imaging system 10 caneject the assembled container 42 (at 26 of FIG. 2).

It should be noted that there may be alternative embodiments in whichflipping of the container is not performed.

At 28 of FIG. 1, the medical practitioner can review the marginassessment width map or other imaging information generated by theimaging system 10. The medical practitioner may manually review theresults generated by the imaging system 10. Alternatively, in someembodiments, the imaging system 10 may provide an analysis of theresults. For example, the imaging system 10 may apply detection methodsthat can detect various different tissue regions within the OCT imagedata. In some embodiments, the imaging system 10 may enable both manualanalysis by the medical practitioner and also provide automated analysisof the images of the tissue specimen.

The imaging system 10 can provide the OCT image data and/or the analysisof the OCT image data to a display 50, for example. The OCT image datathat may be displayed on the display 50 may include a margin assessmentwidth map and/or other imaging information. The medical practitioner canthen interpret the imaging result and determine an appropriate course ofaction. At 30, the routine in the operating room can continue. Theroutine may include routine pathology.

The imaging system 10 can be used for various different applications,such as one or more of OCT surface detection for optimizing an opticalpath scan, sample handling, automated OCT scanning of complex surface aswell as wide field horizontal or vertical stitching of OCT images. Asdescribed with reference to FIGS. 1 and 2, the imaging system 10 can beused to receive a tissue specimen, such as a lumpectomy specimen orother complex specimens, perform scanning and surface profiling of thespecimen, provide imaging results to a medical practitioner during asurgical procedure and eject the container 42 that contains thespecimen.

The scanning of the samples may include selective high or low resolutionscanning. Other embodiments may include scanning of the entire surfaceor a portion of the surface of the sample using another imagingmodality, such as high frequency ultrasound. It will also be understoodthat the imaging system 10 is not limited to imaging of tissue samples,and that any sample that requires the surface to be profiled can bescanned by the imaging system 10. The various structures and techniquesthat allow the imaging system 10 to provide these features will now bediscussed.

Generally, the imaging system 10 can generate OCT images of a sample. Asshown in FIG. 1, the imaging system 10 can be provided in the form of acart 32. The cart 32 can serve as an enclosure for housing a scanningarea 40, such as a chamber within the imaging system 10. The scanningarea 40 is dimensioned and configured for receiving the sample. Theimaging system 10 also comprises a scanning assembly, which can includea sample handling system, within the scanning area 40 for obtaining theOCT image data of at least a portion of the sample. The imaging system10 also comprises a processing module that is in electricalcommunication with the scanning assembly for at least controlling thescanning assembly according to certain OCT imaging parameters. Anexample embodiment of the scanning assembly will be described withreference to FIGS. 4A to 4S. The processing module is generallyconsidered to have an input port through which the processing modulereceives input information such as data that is to be processed. Someexamples of input ports include, but are not limited to, an input pinthat is coupled to a bus or to a communication interface such as aradio, a network connection, a serial connection, a USB connection or aparallel connection. The processing module is also generally consideredto have an output port through which the processing module sends outputssuch as output data to a user, a storage element (e.g. RAM, ROM, etc.),a storage device (a hard drive, a flash drive, etc.) or anothercomputing device, for example. Some examples of output ports include,but are not limited to, an output pin that is connected to a bus or to adisplay device, or a memory element, such as ROM, RAM, and the like.

Referring now to FIG. 3A, shown therein is a perspective view of anexample embodiment of the imaging system 10 in the form of the cart 32.The cart 32 can be used, at least, to hold user interface elements, suchas the display 50 and user input devices 52 and 54 (shown in FIG. 3B),such as, but not limited to, a keyboard and a mouse, and the like forexample. The cart 32 also encloses the sample handling system in thescanning area 40 of the imaging system 10. The cart may present imagingresults to at least one of the user interface elements, such as thedisplay 50, to an operator of the imaging system 10. The user interfaceelements can be presented to the operator at an easily accessibleheight.

The user interface elements may be in electrical communication with theprocessing module. For example, the user interface elements can receiveinput values from the operator of the imaging system 10, such as themedical practitioner, that correspond to OCT imaging parameters forgenerating OCT images of the sample.

The user interface elements can enable an operator of the imaging system10 to select regions of interest on a complex 3D object for OCTscanning. In an example in which the complex 3D object is a sample, theuser interface elements can enable the operator to select the scandensity and/or time constraint for 3D OCT imaging of that sample. Insome embodiments, the user interface elements can include the display 50or a printer so that the imaging system 10 can provide a 3D volumerepresentation of the sample to the operator of the imaging system 10.In some embodiments, the user interface elements can be configured toprovide a 3D representation of a portion of the sample and to allow theoperator to interact with the OCT image data associated with that 3Drepresentation. The OCT image data for that 3D representation mayinclude margin assessment information.

Accordingly, the user interface elements may enable the operator or userof the imaging system 10 to acquire and assess OCT images, which can beused for a variety of purposes including, but not limited to, OCT marginassessment for cancerous tissue samples. The user interface elements canalso enable key imaging specifications to be set prior to conducting theOCT scanning, and can apply various techniques to display the imagingresults to the user and to allow the user to interact with the collectedimaging data. In some embodiments, a manipulable 3D surface map orprofile of the sample may be provided to allow the user to navigatethrough and select images of interest.

The processing module may receive OCT imaging parameters from the userinterface elements, or from a data store such as a hard drive and thelike, and control the operation of the scanning assembly in accordancewith at least the OCT imaging parameters. In some embodiments, theprocessing module may also determine OCT imaging parameters forobtaining the OCT image data of the sample.

The user interface elements, such as user interface elements 50, 52, and54, may be provided so that they are isolated from other components ofthe imaging system 10, such as the scanning area 40, in order to preventcontamination of the sample. An example configuration will now bedescribed with reference to FIG. 3B.

FIG. 3B shows a magnified perspective view of the user input devices 52and 54. The user input devices 52 and 54 may be sterile controls tominimize the chance of contamination during the use of the imagingsystem 10. The sterile controls 52 and 54 allow for a medicalpractitioner, such as a surgeon, to use the imaging system 10 during amedical procedure. Examples of the sterile controls 52, 54 may include,but are not limited to, a keyboard, a mouse, a trackball and atouch-sensitive screen.

As shown in FIG. 3B, the sterile controls 52 and 54 can be housed insidethe cart 32. The sterile controls 52 and 54 can be slidably engaged withan interior surface of the cart 32 so that the sterile controls 52 and54 can slide into and out of the cart 32 for various interactions withthe operator, such as receiving the input values from the operator.

Referring now to FIG. 3C, shown therein is a front view of the imagingsystem 10. The cart 32 includes an access port 46 to the scanning area40 of the imaging system 10. When opened, as shown in FIG. 3A, theaccess port 46 allows an operator to place the sample, such as a tissuespecimen, into the imaging system 10. The tissue specimen may beprovided inside a container 42 and the operator may place the container42 inside the imaging system 10. When the access port 46 is closed, asshown in FIG. 3C, the access port 46 encloses the scanning area 40 toprevent contamination of the tissue specimen while it is inside theimaging system 10. The access port 46 may be closed during at least apart of the operation of the sample handling system. The access port 46can also allow a maintenance person, or other operators, to service theimaging system 10.

Referring now to FIG. 3D, shown therein is a magnified view of a wheellock 60 of the imaging system 10. The wheel lock 60 can be used tomaintain the imaging system 10 at a fixed position by locking the wheelsof the cart 32 at a desired location. The wheel lock 60 can prevent thecart 32 from engaging in any undesired movement that can affect theoperation of the imaging system and negatively impact the quality of theOCT images.

Referring now to FIG. 3E, shown therein is a rear perspective view ofthe imaging system 10. The cart 32 may include a service access area 70at the rear portion of the cart 32. The service access area 70 can allowthe maintenance person, or other operators, to service the imagingsystem 10.

As briefly described, the imaging system 10 can include a scanningassembly within the scanning area 40 for facilitating the capture of OCTimage data of the sample and the generation of corresponding OCT imagesof the sample. The sample may include a tissue specimen of a tumor or asample having a complex 3D surface.

The scanning assembly can include a sample handling system that canmanipulate the sample, or the container 42 holding the sample, in orderto facilitate optical scanning of the surface of the sample whilemaintaining the integrity of the sample. The sample handling system caninclude a support area for receiving the sample or container 42, andvarious actuators for manipulating the sample via the support area byholding the sample in certain orientations during the imaging process.

In some embodiments, the scanning assembly may include a three-axis (X,Y, Z) stage for adjusting a position of at least one of the scanninghead and the sample. The three-axis stage can facilitate variousoperation of the scanning assembly, such as surface scanning of aselected region of interest on the sample or an exposed surface of thesample. In some embodiments, the scanning assembly can include anattachment plate, or a container adapter, for securing the container 42to the three-axis stage.

The scanning assembly can also include various mechanical modules ormechanisms for providing the necessary degrees of freedom for imagingthe sample. The scanning assembly can also lock and control the speed atwhich the mechanisms operate. These features can be especially importantin the case of power interruptions. The various types of mechanisms thatcan be used include, but are not limited to, at least one of poweredsafety brakes, constant force elements or counterbalances, andelectrical energy storage elements, for example.

Referring now to FIGS. 4A to 4S, shown therein are illustrations ofdifferent views of an example embodiment of a scanning assembly 80 thatcan be used in the imaging system 10 for scanning the sample.

FIG. 4A is a front view of the scanning assembly 80 and FIG. 4B is arear view of the scanning assembly 80 of FIG. 4A. As shown, the scanningassembly 80 includes a frame 82 upon which various actuators ortranslation mechanisms are coupled to provide various degrees of motionto the scanning assembly 80. With the various degrees of motion offeredby the mechanisms, the scanning assembly 80 can acquire raw OCT data ofthe sample from at least two independent directions, as will bedescribed.

The scanning assembly 80 also includes a scanning head 84, a firsttranslation mechanism 86, a tilt mechanism 87, a second translationmechanism 88, and a trans-rotational mechanism 90. The various arrows72, 74, 76, 78 and 79 in FIG. 4A show the various degrees of freedom ofthe scanning assembly 80.

The movement of each of the first translation mechanism 86 and thesecond translation mechanism 88 can be facilitated with a beltcomponent. For example, as illustrated in FIGS. 4A, 4R and 4S, a beltcomponent 85 can help control the translation motion of the firsttranslation mechanism 86. Similarly, as shown in FIGS. 4A and 4B, thebelt components 89 a and 89 b can be mounted on different portions ofthe scanning assembly 80, such as the base board 81 and the secondtranslation mechanism 88, respectively, to control the translationmotion of the second translation mechanism 88. The belt components 85,89 a and 89 b can also help enhance the stability of the firsttranslation mechanism 86 and the second translation mechanism 88 whenthey are in motion. It will be understood that other similar componentsmay be used to control and stabilize the motion of the first translationmechanism 86 and the second translation mechanism 88.

The frame 82 includes several support brackets 83 to attach the secondtranslation mechanism 88 to a base board 81 of the frame 82. The supportbracket 83 may include a main support bracket 83 a and a secondarysupport bracket 83 b (shown also in FIG. 4R), for example.

The base board 81 may be an optical breadboard. The optical breadboardcan be advantageous because it is generally associated withhigh-flatness and rigidity and thus, is appropriate for mounting theframe 82.

FIG. 4C is a perspective view of the trans-rotational mechanism 90 and asupport area 92. The support area 92 may include a container adapter 92Bfor receiving the container 42 via a container support 92A on thecontainer 42. The container support 92A, as shown in FIG. 4C, may hold asample 94. Together, the container 42 and the support area 92 can holdthe sample 94 in a stable manner during the operation of the scanningassembly 80.

The trans-rotational mechanism 90 may also include a rotational stage90A attached to a linear stage 90B. As shown in FIGS. 4A and 4B, thelinear stage 90B is mounted to the base board 81. The trans-rotationalmechanism 90 is intended to manipulate the container 42 to apply atleast one of a rotational movement and a translational movement, asgenerally illustrated by the respective arrows 74 and 76 in FIGS. 4A and4C, to the container 42.

The rotational stage 90A may be driven by a direct-drive motor and mayinclude an optical encoder for directing its rotational movement. Onebenefit of using the direct-drive motor is the elimination of backlash.The optical encoder can control the rotational movement of therotational stage 90A by converting an angular position of the motorshaft to a digital code. The digital code corresponds to a rotationalposition for the rotational stage 90A. Therefore, when the rotationalstage 90A receives the digital code from the optical encoder, therotational stage 90A can rotate to the position corresponding to thatdigital code. In some embodiments, the optical encoder can provide anaccuracy of +/−3.9 arcsec.

Generally, the linear stage 90B should be capable of translating thesample 94 a distance that approximately corresponds to a diameter of thesample 94. A typical diameter of samples may be about 120 mm. In someembodiments, the linear stage 90B may be a direct-drive linear motorstage, such as Model DDS220 by Thorlabs™. It will be understood thatother mechanical devices that provide at least linear translation withinthe scanning assembly 80 may be used.

FIG. 4D is a perspective view of the container adapter 92B and FIG. 4Eis a cross-sectional exploded view of the container support 92A and thecontainer adapter 92B. As shown in FIG. 4D, the container adapter 92Bhas four holes, namely 96A to 96D, and two engagement members 98A, 98B,such as spring plungers or other similar engagement components. Theengagement members 98A and 98B can be located at approximately oppositesides of the container adapter 92B. It will be understood that theillustrated number of holes 96 and engagement members 98 are merelyexamples and that fewer or greater number of holes 96 and engagementmembers 98 may be used.

Referring now to FIG. 4E, the container support 92A includescomplementary latches 170A, 170B for securably engaging withcorresponding engagement members 98A, 98B, respectively. For example,when spring plungers are used as the engagement members 98A, 98B on thecontainer adapter 92B, the spring plungers 98A and 98B press against thelatches 170A and 170B, respectively, to secure the container support 92Ato the container adapter 92B. It will be appreciated that differentspring plungers can exert a different range of forces, such as 1 to 2.5lb-f, 2 to 5 lb-f or 4 to 10 lb-f. For securing the container support92A to the container adapter 92B, spring plungers that can exert atleast 4 lb-f can be used.

The scanning head 84 can obtain raw OCT data of the sample provided atthe support area 92. As shown in FIG. 4A, the scanning assembly 80 has agantry-like design that enables the scanning head 84 to effectively moveabout the sample at various angles and to acquire raw OCT data of thesample 94 at the various angles. In at least some embodiments, thescanning head 84 can be pivotally mounted to the frame 82 so that thescanning head 84 can pivot or rotate about an axis to vary the angle ofview of the sample 94, such as shown by arrow 79. Accordingly, thescanning head 84 can be pivotally adjustable with respect to the sample94.

FIG. 4F is a front-side perspective view of the scanning head 84 and thetilt mechanism 87 and FIG. 4G is a rear perspective view of the scanninghead 84 and the tilt mechanism 87 of FIG. 4F. As shown in FIGS. 4J and4K, the tilt mechanism 87 can be attached to a home device 171 (shown inFIGS. 4H and 4I) via fasteners shown generally at 85. As will bedescribed, the tilt mechanism 87 can be attached to the firsttranslation mechanism 86 via the home device 171.

The scanning head 84 may be a probe within the Telesto™ line of OCTimaging systems provided by Thorlabs, for example. It will be understoodthat other OCT imaging probes may be similarly used. As generallyindicated by the arrow 79 in FIG. 4A, the tilt mechanism 87 can rotatethe scanning head 84 within a range of approximately 0 to 180 degreesabout an axis of rotation that is substantially perpendicular to atranslation plane of the first translation mechanism 86 and the secondtranslation mechanism 88. In some embodiments, the maximum rotationalmovement of the tilt mechanism 87 can be limited to a range of −2 to+182 degrees by including a ridge 172 at the home device 171 (as shownin FIGS. 4H and 4I, which illustrate different views of the home device171).

The tilt mechanism 87 may include an actuator that is harmonic-driven.By using a harmonic-drive actuator, the tilt mechanism 87 is able toproduce a high torque with minimal backlash and a very high accuracy inits movement. The harmonic-drive actuator can include anincremental-type encoder.

The scanning head 84 can also be operably coupled to each of the firsttranslation mechanism 86 and the second translation mechanism 88 so thatthe scanning head 84 is shifted in a first linear direction and a secondlinear direction with respect to the sample 94. The first translationmechanism 86 allows the scanning head 84 to be shifted in the firstlinear direction with respect to the sample 94 at the support area 92,and the second linear translation mechanism 88 allows the scanning head84 to be shifted in the second linear direction with respect to thesample at the support area 92. The second linear direction may besubstantially perpendicular to the first linear direction and alsoco-planar with the first linear direction.

In the example shown in FIG. 4A, the first linear direction has an upand down or vertical orientation with respect to the sample 94 at thesupport area 92 and the second linear direction has a left and right orhorizontal orientation with respect to the sample 94. Accordingly, thefirst translation mechanism 86 can raise and lower the scanning head 84in the first linear direction, and the second translation mechanism 88can shift the scanning head 84 left and right along the second lineardirection.

The first translation mechanism 86 and the second translation mechanism88 allow for various OCT images of the sample to be taken from a firstplane, which is a two-dimensional (2D) imaging plane. Thetrans-rotational mechanism 90 allows the sample to be moved in a linearand rotational fashion in a second plane. The second plane issubstantially perpendicular to the first plane. The trans-rotationalmechanism 90 can move the sample 94 linearly towards and away from thefirst plane while also allowing the sample 94 to be rotated with respectto the scanning head 84. The scanning head 84 may also be pivotallyadjustable about an axis of rotation that is substantially perpendicularto the first plane. As a result, the scanning head 84 can capture rawOCT data of various depths around various surfaces of the sample.

Each of the first translation mechanism 86 and the second translationmechanism 88 will now be described.

FIG. 4L is a perspective view of the first translation mechanism 86 withthe tilt mechanism 87 and the scanning head 84, and FIG. 4M is aperspective view of the first translation mechanism 86.

The first translation mechanism 86 may include an encoder for directingthe translation movement. The first translation mechanism 86 may alsoinclude one or more homing switches. The encoder may be an incrementaltype encoder or other similar encoders. The homing switch may facilitatecalibration of the scanning assembly 80, which can help improve theaccuracy of the spatial coordinates used with the scanning assembly 80.The first translation mechanism 86 should be capable of moving adistance that corresponds, at least, to a height of the sample 94. Thedistance may also take into account the movement of the tilt mechanism87. For example, a sample 94 with a height of 80 mm may require aminimum translation movement of 180 mm by the first translationmechanism 86 since the operation of the tilt mechanism 87 can provide anadditional vertical movement distance of approximately 100 mm. Ingeneral, the height of the first translation mechanism 86 should beminimized as much as possible so that the overall size of the scanningassembly 80 can be as small as possible.

As shown in FIG. 4M, the first translation mechanism 86 includes a baseplate 173 and a carriage plate 174. The components of the firsttranslation mechanism 86 are attached, either directly or indirectly, tothe base plate 173, which is then attached to the frame 82 of thescanning assembly 80. The carriage plate 174 can securably receive thetilt mechanism 87 via the home device 171 (as shown in FIG. 4L).

As shown in FIGS. 4L and 4M, the first translation mechanism 86 includesa linear bearing structure 179 along which the carriage plate 174 istranslated. The linear bearing structure 179 may be provided with anair-bearing or a crossed-roller bearing, for example, to ensure accuracyand smoothness in the movement. Another example linear bearing structure179 may be linear guides, such as 179A and 179B shown in FIGS. 4L to 4N.The linear guides may be caged-ball linear guides. Compared with linearbearing structures 179 formed with the air-bearing or the crossed-rollerbearing, the linear guides are less complicated to design and lessexpensive while also providing a fairly smooth and reliable translationmovement.

The first translation mechanism 86 also includes a pair of linearactuators 175A, 175B. As shown in FIGS. 4L to 4N, the linear actuators175A, 175B may be mounted to the base plate 173 with a slider clamp 177to help absorb any shock or vibrations resulting from the movement ofthe linear actuators 175A, 175B or any other mechanical components inthe scanning assembly 80.

The linear actuators 175A, 175B may be operated by a brushless linearmotor, such as motor 178, to minimize back-lash. In some embodiments,the linear motor 178 can include ironless stator coils to furtherminimize vibration in order to maximize smoothness in the movement.Example motors may include motor model DX20B-C2, DX20B-C3 or DX20B-C4 byPBASystems™. Thermal resistance associated with the motor 178 during usemay also be considered when selecting the motor for the firsttranslation mechanism 86.

In some embodiments, the dimensions of the linear actuators 175A, 175Bcan be selected to accommodate the movement required for the firsttranslation mechanism 86.

FIG. 4N is a partial front view of the first translation mechanism 86illustrating components below the carriage plate 174. It will beunderstood that the carriage plate 174 appears transparent for thepurpose of illustrating components underneath, and that the carriageplate 174 may or may not be formed of a transparent material.

As shown in FIG. 4N, the first translation mechanism 86 may include alinear encoder 176 for controlling the movement of the linear actuators175A, 175B. In some embodiments, the linear encoder 176 can determine anintended position for the first translation mechanism 86 based onposition information provided by the operator via a control system ofthe scanning assembly 80 (the control system will be described later).The linear encoder 176 can then initiate the operation of the motor 178and the linear actuators 175A, 175B to move the first translationmechanism 86 to the intended position. The linear encoder 176 may also,in some embodiments, provide a feedback to the control system forverifying the position of the first translation mechanism 86.

Generally, the linear encoder 176 can provide a response speed ofapproximately 2 m/s, a resolution of approximately 1 μm and an accuracyof approximately 5 μm. In some embodiments, the linear encoder 176 canprovide an accuracy of 1 μm, a resolution of 50 nm and a response speedclose to 2 m/s. Example linear encoders 176 can include an encoder fromthe Mercury II™ 5000 series, for example. It will be understood thatother similar encoders may be used.

The first translation mechanism 86 can also include a dowel pin 345 (seeFIGS. 4N and 4O) to help align the first translation mechanism 86. Thedowel pin 345 may also enable a larger interval of movement of the firsttranslation mechanism 86.

In some embodiments, the first translation mechanism 86 may include acounterbalance component (not shown) for balancing the dynamics of thevertical axis. The counterbalance component may stabilize the scanninghead 84 after the scanning head 84 has moved. For example, thecounterbalance component can reduce the resonant effects caused by thestopping and starting of the movement of the scanning head 84. Thecounterbalance component may be a magnetic spring counterbalance.Magnetic spring counterbalances can be advantageous due to their passivenature, which can ensure a defined function or position of the device ina power-off condition. For example, a linear actuator 175A or 175B canbe pushed in or pulled out with a constant force and to a predefinedposition. As a result, the counterbalance component may help ensuresafety in the case of a power fault or interruption by locking and/orcontrolling the speed at which the first translation mechanism 86operates.

In some embodiments, the counterbalance component may include dualcounter-balances to accommodate any required forces and to minimize anyabbe error.

Reference will now be made to FIGS. 4O to 4Q for describing the secondtranslation mechanism 88. FIG. 4O is a perspective view of the secondtranslation mechanism 88. FIG. 4P illustrates a bottom perspective viewof a horizontal encoder 340 for the second translation mechanism 88 ofFIG. 4O and FIG. 4Q illustrates a rear perspective view of thehorizontal encoder 340.

Similar to the trans-rotational mechanism 90 and the first translationmechanism 86, the second translation mechanism 88 may be capable oftranslating the scanning head 84 at least along a diameter of the sample94. As noted, the typical diameter of samples may be about 120 mm. Thetranslation distance required for the second translation mechanism 88may be longer in some embodiments to accommodate different factors, suchas an optical focal length and horizontal movement due to the tiltmechanism 87.

The second translation mechanism 88 can include a horizontal base plate341 for securably receiving the first translation mechanism 86. Thesecond translation mechanism 88 can also include bumpers 342 at eitheredges of the second translation mechanism 88 to minimize any vibrationsthat may be caused by the translation movement and to limit thetranslation path of the second translation mechanism 88.

Many of the components provided in the second translation mechanism 88are similar to those described with reference to the first translationmechanism 86, and therefore, will not be repeated. For example, thesecond translation mechanism 88 includes linear guides 343A and 343Bthat can be similar to the linear guides 179A and 179B of the firsttranslation mechanism 86 and a linear motor 344 similar to the linearmotor 178. The second translation mechanism 88 also includes the dowelpin 345 to help align the second translation mechanism 88.

It will be understood that although these components are similar foreach of the first and second translation mechanisms 86, 88, they do notneed to be the same in a particular scanning assembly 80.

Referring now to FIG. 4S, which is a perspective view of the scanningassembly 80 of FIG. 4A while in an example operation. As shown in FIG.4S, the scanning head 84 is tilted by the tilt mechanism 87. Thescanning head 84 is also translated to an edge of the second translationmechanism 88 and to approximately a lower middle portion of the firsttranslation mechanism 86.

The scanning assembly 80 can also provide biasing of the mechanicalmovement to facilitate the typical image orientations expected acrosstissue margins. For example, the various actuation mechanisms cansupport rapid acquisition of raw OCT data for multiple parallel B-scansso that these images can be stacked parallel to each other to create aC-scan. In computed tomography, it is generally understood that a B-scanis a 2D slice through the sample and a C-scan is a typical 3Dvisualization of a volume of the sample.

As noted, the scanning assembly 80 is in electrical communication withthe processing module and thus, the processing module can receive theraw OCT data captured by the scanning assembly 80 and generate widefield OCT images based on the received raw OCT data.

In some embodiments of the imaging system 10, the scanning head 84 mayinclude a red wavelength laser source light for scanning and mappinghuman tissue samples. For example, in the application of scanning humanbreast tissue, the scanning head 84 may be a 660 nm laser.

The scanning head 84 may be a line scanner, for example. Furthermore, aminus 250 micron calibration in the height values may be used to accountfor any tissue penetration and the exposure that may be set on the linescanner to either automatically determine the necessary exposure time orthat the exposure time should be set to roughly 300 μs for fatty tissue.It has also been found that height is not a contributing factor to thevariation in measurements, so the distance that the sample is away fromthe line scanner may be determined by taking into account the expectedheight of the sample as well as the clearing distance of the beam.

Referring now to FIG. 5, shown therein is a block diagram 100 of anexample embodiment of hardware components that can be used with theimaging system 10. The imaging system 10 can include an OCT module 102,a probe module 104, a scanning module 106, a processing module 108 andan enclosure module 110. It should be noted that the block diagram 100is just one example embodiment for the imaging system 10 and that otherconfigurations are possible. For example, some components can be groupedtogether, grouped differently and/or additional components may be useddepending on the particular application of the imaging system 10.

The OCT module 102 can include an optical and control module 112 and acamera Input/Output (I/O) module 114. The optical and control module 112can include optical hardware for capturing raw OCT data of the sampleusing the probe module 104 and a processor for controlling the opticalhardware. The OCT module 102 generally controls the acquisition of theraw OCT data. For example, the camera 114 may be synced with a lightsource in the optical and control module 112 and with the probe module104.

The probe module 104 can include an opto-mechanical control module 116.The opto-mechanical control module 116 may include opto-mechanicalhardware, such as a scanning lens and imaging optics, for creating anOCT image of the sample and a processor for controlling theopto-mechanical hardware. The probe module 104 corresponds to thescanning head 84 shown in FIG. 4.

The probe module 104 can include a set of rastering mirrors that canscan the light source from the OCT module 102 along a surface of thesample. Each scan by the probe module 104 (or the scanning head 84) caninclude 1024 A-scans over a 15 mm range. In some embodiments, the probemodule 104 can repeat the scan at 0.5 mm intervals to produce a lowresolution volume image (i.e., a C-scan) over a 15×15 mm area. The probemodule 104 can be coupled and synced with the scanning module 106. Theprobe module 104 can also alter a position of each C-Scan in order tocapture raw OCT data for as much of the sample surface as possible.

The scanning module 106 can include a mechanical and control module 118.The scanning module 106 can include the scanning assembly 80 of FIG. 4A.For example, as described with reference to FIGS. 4A to 4S, the scanningassembly 80 includes various translation mechanisms, such as translationmechanisms 86, 88 and 90, that can position the scanning head 84 and/orthe container 42 in various positions so that as much of the surface ofthe sample 94 can be imaged and images at various angles at a depth of aportion of the sample 94 can also be obtained. For example, the scanningmodule 106 can position the scanning head 84 over the sample 94 tocapture a series of scans at various heights with respect to the surfaceof the sample 94. As will be described, the series of scans at differentheights can ensure that as much useful data is captured for the sample94.

As briefly described, the processing module 108 can control theoperation of the imaging system 10. The processing module 108 generallyincludes a control system 120 and an application system 122.

The control system 120 can include a control module 126 and an initialdigital signal processor (DSP) module 124. The control module 126, theinitial DSP module 124 or the processing module may each include one ormore processors or other dedicated circuitry depending on theconfiguration, purposes and computing requirements of the imaging system10.

It should be understood that although only one control module 126 isshown within the control system 120 of FIG. 5, the control system 120can include more control modules 126 depending on the design andconfiguration of the imaging system 10. For example, the control system120 can include a separate control module 126 to operate with each ofthe OCT module 102, the probe module 104 and the scanning module 106.The number of control modules 126 provided in the control system 120 canalso depend on the amount of computing power that is required by theprocessing module 108 and/or the imaging system 10 as a whole.

The initial DSP module 124 can control the relative motion between thescanning head 84 and the sample to obtain the raw OCT data forgenerating the wide field OCT images of the sample. The initial DSPmodule 124 can perform some initial digital signal processing on the rawOCT data that is provided by the OCT module 102 and generatepre-processed OCT data of the sample. The initial digital signalprocessing may include filtering and amplification.

The application system 122 includes an application 130 and an imageprocessing module 128. The application system 122 can be an operatingsystem, for example. In use, the processing module 108 can execute theapplication system 122 to run the application 130 for enabling captureof the raw OCT data by the OCT module 102, the probe module 104 and thescanning module 106. The image processing module 128 can use theacquired raw OCT data to create a wide field OCT image of the entiresurface of the sample or specific regions of interest of the sample. Theapplication system 122 can also manage user interactions and datastorage.

The enclosure module 110 includes I/O components 132, a power module 134and an enclosure 136. The I/O components 132 can include input andoutput devices, such as the user input devices 50 and 52. The I/Ocomponents 132 can include hardware for relaying I/O signals between anoperator of the imaging system 10 and the imaging system 10. In someembodiments, the I/O components 132 may provide a user interface withwhich the operator can inspect generated OCT images.

As described, the enclosure 136, such as the cart 32, can house andprotect the physical components of the imaging system 10. The powermodule 134 receives power from a power source, such as a three-phasemains line, a battery or a power generator, and transforms the receivedpower so that it can be used by the various components of the imagingsystem 10.

An example operation of the imaging system 100 shown in FIG. 5 will nowbe described. After the imaging system 100 receives the container 42holding a sample into the enclosure 136, the I/O component 132 canreceive inputs from the operator. The inputs received via the I/Ocomponent 132 may include physical dimension data associated with thesample in the container 42. Alternatively, the input may indicate that arepresentation needs to be generated for the sample by the camera 114.

In response to the inputs received at the I/O component 132, theapplication 130 can process the received inputs to generate scanningparameters for the control module 126. Based on the scanning parameters,the control module 126 can forward the control commands to each of theOCT module 102, the probe module 104 and the scanning module 106 forcontrolling the operation of the imaging system 100. The controlcommands may include movement commands indicating how the probe module104 should be moved by the scanning module 106 and data acquisitioncommands indicating the amount and type of raw OCT data that needs to becollected by the OCT module 102, for example.

The raw OCT data collected by the OCT module 102, the probe module 104and the scanning module 106 can then be provided to the initial DSPmodule 124 for processing. The initial DSP module 124 can convert theraw OCT data into initial OCT images, for example. The initial OCTimages can then be further processed at the image processing module 128.The processing of the initial OCT images will be described withreference to at least FIGS. 7A to 7F.

Reference will now be made to FIGS. 6A and 6B, which show differentviews of a prototype imaging system 140 for obtaining OCT image data.The prototype imaging system 140 includes a monitor 142 for displayingimaging results and/or analysis, and a prototype scanning assembly 148.

The prototype imaging system 140 was developed to facilitate preclinicaland clinical studies. During those studies, the prototype imaging system140 was used to collect quantitative imaging data from tissue-simulatingphantoms and from animal-model and human tissue samples. Based on areview of the collected imaging data, the performance of the imagingsystem 140 was improved.

The prototype imaging system 140 shown in FIGS. 6A and 6B is based onSpectral-Domain OCT (SD-OCT), which can maximize the sensitivity andspeed of scanning of an image. FIG. 6C shows an example schematic 150 ofthe SD-OCT system.

The SD-OCT system 140 includes a broadband light source 152, a beamsplitter 154, a sample arm assembly 158, a reference arm assembly 156, adiffraction grating 162 and a detector array 160 that provides ‘N’number of bins (or ‘N’ number of output samples). The broadband lightsource 152 is coupled through a fiber to the 50/50 beam splitter 154.The sample arm assembly 158 directs one half of the source light intothe tissue sample which generates a reflected sample light signal. Thesecond half of the source light is directed to a reference mirror in thereference arm assembly 156. The reference mirror can then generate areflected reference light signal. In the context of the imaging system10, the SD-OCT system 140 components may be part of the scanning head84.

The light signals reflected from the reference mirror at the referencearm assembly 156 and from the tissue sample at the sample arm assembly158 are then combined at the beam splitter 154 to form an interferencepattern. The combined light is then sent to the detector array 160 wherethe interference between the reflected sample light signal and thereflected reference light signal can be measured. The output of thedetector array 160 is then processed and used to create an OCT A-scan ofthe sample. The beam from the broadband light source 152 may then beswept across one or more different portions of the surface of the sampleto generate complete or partial 2D or 3D OCT images.

Operation of the imaging system 10 for generating wide field OCT imageswill now be described with reference to FIGS. 7A to 11F.

With respect to analyzing tissue specimens, a wider view of the tissuespecimen is generally useful in creating context for different tissuefeatures. The field of view of a conventional OCT image reconstructedbased on A-scans is limited in terms of the useful viewing range. Withconventional techniques, the surface of the tissue specimen must be acertain distance from the OCT camera to make use of the full imagingwindow. However, the surface of the tissue specimen is often irregular,making it difficult to gather information over the entire surface. Inaddition, if the OCT camera is too close to the tissue specimen, thenthere will be undesirable effects in the OCT images, such as“wrap-around” effects, and if the OCT camera is too far from the tissuespecimen, there will be little or no OCT signal.

To create a wide field OCT image, the imaging system 10 may beconfigured to scan the sample at different locations. The imaging system10 may follow a certain scanning pattern, such as a raster pattern forexample. The scanning pattern can generally be provided to the imagingsystem 10 as a series of control commands for adjusting a position ofthe scanning head 84. For example, the control commands can adjust aposition of the series of mirrors at the probe module 104. In general,the scanning pattern can be uniquely generated to optimize scanning timeand resolution in regions of interest of the sample 94. At each positionof the surface of the sample (e.g., a position can be defined by a X,Y,Zcoordinate in a Cartesian coordinate system), the imaging system 10 cancollect raw OCT data at various distances from the surface of thesample. The imaging system 10 can also record spatial location for eachOCT scan.

The imaging system 10 can then process the raw OCT data to generate OCTimages that can then be combined together using one or more stitchingprocesses that will be described with reference to FIGS. 7A to 11F togenerate a composite OCT image. Example combination methods may includeat least one of a wide field vertical stitching and horizontalstitching.

In some embodiments, as will be described with reference to FIG. 7F, theimaging system 10 may perform further processing so that the compositeOCT image or the reconstructed OCT images have improved image quality.

Referring now to FIG. 7A, shown therein is a flowchart of an exampleembodiment of a wide field OCT imaging method 180. The imaging system 10may apply various techniques, such as stitching of OCT images and otherprocessing techniques for generating wide field OCT images of a sample.

At 182, the imaging system 10 can acquire raw OCT data of the sample andgenerate OCT images using the acquired raw OCT data.

In order to collect the raw OCT data over at least a portion of thesample, the imaging system 10 can operate the scanning assembly 80 alongwith a position measuring device. The position measuring device mayinclude position sensors or cameras. For example, the position measuringdevice may be a laser based position sensor that is either a point-basedmeasurement tool or a line scanner. For example, the laser basedposition sensor may be a laser position finder or scanner that isattached to the scanning head 84 or mounted separately, such as to achassis, for example. Examples of cameras that may be used include, butare not limited to, one or more standard charge-coupled device (CCD)cameras that can capture images of the sample. The sample may also berotated to facilitate the operation of the camera. The images capturedby these cameras can be reconstructed to form a 3D representation of thesample. Like the laser based position sensor, the camera may be attachedto the scanning head 84 or mounted separately. The 3D representation ofthe sample provides an indication of an overall surface of the sample tobe imaged.

The position measuring device can measure and register at least aportion of the surface of the sample. Based on the measurements providedby the position measuring device, the scanning assembly 80 may generatea representation of the sample.

The representation of the sample may be a coarse spatial representation.The representation may include at least one of a surface map of thesample, a point cloud representation of the sample, and an interpolatedsurface of the point cloud representation. The representation of thesample may be generated using various triangulation methods thatinterpolate the surface positions provided by the position measuringdevice.

For example, the position measuring device can be a laser sensor. Thelaser sensor is positioned over a sample so that the laser sensor canmeasure a height from various positions along a surface of the sample.The various positions along the surface of the sample may correspond toX,Y coordinates in the Cartesian coordinate system. A representation ofthe surface of the sample may be generated by interpolating each of themeasured height data based on the various positions along the surface ofthe sample.

The representation can facilitate the assessment of the specific regionsat which high resolution raw OCT data is being captured. As will bedescribed, the imaging system 10 may also use surface maps of the samplefor maintaining focus throughout the OCT scanning process.

In some embodiments, the imaging system 10 can use the representation ofthe sample for developing a scan path for the scanning head 84 forcapturing raw OCT data of a region of interest of the sample. Theimaging system 10 can also use the representation for preventingcollision of the scanning head 84 with the sample. The imaging system 10can guide the scanning head 84 towards the sample in a safe manner. Thecollision detection method may be used during and after the scanningpath has been created. For example, the imaging system 10 may require aminimum distance between the scanning head 84 and the sample so that thescanning head 84 does not collide with the sample or any of the othercomponents of the scanning assembly 80, such as a portion of the frame82 or any of the actuation mechanisms 86, 88 and 90.

Reference will now be made to FIG. 7B, which shows a flowchart of anexample embodiment of a method 182 of acquiring raw OCT data. Generally,the imaging system 10 may operate the scanning assembly 80 to acquirethe raw OCT data in order to generate a plurality of OCT images of atleast a portion of the sample (i.e. the region of interest) based on atleast the generated representation of the sample, such as the surfacemap. In some embodiments, the imaging system 10 may also operate thescanning assembly 80 in accordance with user inputs provided via theuser interface elements. The user inputs may provide parameters for thewide field OCT images, for example.

At 202, the imaging system 10 can locate a portion of the sample that isto be imaged (e.g., a region of interest). The imaging system 10 maydetermine the region of interest based on user inputs provided via theuser interface elements, for example.

At 204, the imaging system 10 can adjust the scanning head 84 so that asurface of the region of interest of the sample is within view of thescanning head 84. As noted, the imaging system 10 can operate thescanning assembly 80 according to at least one of the surface map andthe user inputs. At 206, the imaging system 10 records the position ofthe scanning head 84. The position may be indicated using X, Y and Zcoordinates in the Cartesian coordinate system, or other coordinatesystems. At 208, the imaging system 10 can record the raw OCT data ofthe region of interest from the position of the scanning head 84.

At 210, once the imaging system 10 has recorded the raw OCT dataassociated with the region of interest, the imaging system 10 can adjustthe position of the scanning head 84 axially towards the sample.

By adjusting the position of the scanning head 84 axially towards thesample, the imaging system 10 can capture the raw OCT data for a seriesof vertical OCT images that includes an initial OCT image and one ormore vertical neighbouring OCT images. Each of the OCT images in theseries of vertical OCT images is associated with a different scanningdistance between the scanning head 84 and the surface of the sample. Aswill be described, the imaging system 10 may obtain the raw OCT data forthe series of vertical OCT images such that the focus is maintainedsubstantially constant.

In OCT systems, the interference signal between the sample signal, suchas the signal from the sample arm assembly 158 of FIG. 6C, and thereference signal, such as the signal from the reference arm assembly 156of FIG. 6C, increases in intensity as their respective optical pathlengths of these arms match in distance. When the optical path lengthsof each of the sample signal and the reference signal are matched, thereference arm (which can also be referred to as a delay line) ispositioned at a zero delay position with respect to the sample arm.

The zero delay position corresponds to a position where the interferencepattern is at a maximum. The resulting image quality at the zero delayposition is therefore generally superior compared to the rest of theimage. Conventional OCT imaging techniques generally involve positioningthe reference arm with respect to a focal point of a sample so that thesurface of the sample corresponds to an upper or top edge of an imagingwindow. The zero delay position in the imaging window for conventionalOCT imaging systems would correspond to the upper edge of the imagewindow

In the methods and systems described herein, the zero delay position maybe adjusted to be similar to the focal point position of the sample thatis set at a predetermined distance below the surface of the sample. Thatis, the zero delay position may be below the surface of the sample, likethat of the focal point position. Since the focal point position of thesample is generally a part of the region of interest, adjusting the zerodelay position to be similar to the focal point position of the samplecan improve the quality of the resulting OCT images by maximizing theinterference signal in the region of interest of the sample. Generally,the zero delay position can be positioned at depths of penetration up toabout 2 mm, for example. Adjustment of the reference arm assembly, suchas reference arm assembly 156, can help adjust the focal point position,which is in the region of interest, so that it corresponds to apredefined region of the imaging window.

In some embodiments, wrap-around artifacts may appear in the regionabove the zero delay position of a given OCT image. The wrap-aroundartifacts may include the image rotated at 180 degrees. As will bedescribed, further image processing and adjustments can be applied tothe given OCT image to reduce wrap-around artifacts in the region abovethe zero delay position.

In order to maintain constant focus across a sample, the imaging system10 can adjust the scanning head 84 based on the surface map of thesample. For example, for each X,Y position on the surface of the sample,the imaging system 10 can axially adjust the scanning head 84 so thatthe focus is initially at the surface of the sample. For samples with anuneven surface, the imaging system 10 may position the scanning head 84at various different heights across the surface of the sample. The focalpoint positioning technique can be beneficial since the surface of mostturbid samples is non-uniform which makes maintaining the focal point ofthe resulting OCT images at a constant depth a difficult task during OCTscanning. The focal point positioning technique described herein alsoavoids the use of dynamic surface tracking for focal point positioning,which is advantageous since dynamic surface tracking can be complex andcomputationally intensive.

Once the imaging system 10 has positioned the scanning head 84 so thatthe focus is at the surface of the sample, the imaging system 10 mayfurther move the scanning head 84 by a scan adjustment distance towardsthe sample so that the focus is beneath the surface of the sample. Theportions of the resulting OCT images that are in focus will generallycorrespond to a region at approximately a scan adjustment distance belowthe upper edge of the imaging window (examples of which are shown in therectangular areas in the images of FIGS. 8D and 9A).

The scan adjustment distance may vary depending on the specifications ofthe imaging system 10 (e.g., type of lens, error estimation for theresulting images, etc.), the type of specimen (e.g., different tissuespecimens may have different density), the purpose for the imaging(e.g., tissue differentiation for excised tissues with tumors, etc.),and other considerations. In some embodiments, the scan adjustmentdistance may be approximately half of the error estimation for theresulting images.

For example, as shown in FIGS. 8A and 8B, the scan adjustment distancemay depend on a numerical aperture value associated with the lens of theimaging system 10. FIG. 8A is a schematic 350A of an optical path for alens with a high numerical aperture and FIG. 8B is a schematic 350B ofan optical path for a lens with a low numerical aperture. As isgenerally understood, a location of a focus can vary depending on thenumerical aperture of the lens. As shown in FIGS. 8A and 8B, a lens witha higher numerical aperture will have a closer focus 351A compared to afocus 351B for a lens with a lower numerical aperture.

A region on either side of the focus can be referred to as a depth offield or a focused portion since the lens is able to capture the bestimage quality, or useful image data, from that region. The size of thedepth of field also varies with the numerical aperture of the lens. Asshown in FIG. 8A, for example, the depth of field 352A for a lens withthe higher numerical aperture is smaller than the depth of field 352Bfor a lens with a lower numerical aperture (FIG. 8B).

Referring now to FIG. 8C, shown therein is a schematic of the scanninghead 84 capturing an example OCT image 278. As described with referenceto FIGS. 8A and 8B, a focused portion 278 a for the OCT image 278 canvary based on a lens of the scanning head 84. Accordingly, in order forthe imaging system 10 to capture sufficient raw OCT data at variousdepths of the sample, the imaging system 10 can adjust an axial positionof the scanning head 84 so that different axial (i.e. vertical) regionsof the sample appear in the focused portions 278 a of the imagingwindow. The imaging system 10 may continue to adjust the axial positionof the scanning head 84 until sufficient raw OCT data has been capturedfor the sample.

FIG. 8D illustrates a series 280 of OCT images for a sample surface 279.Each image 280 a to 280 c in the series 280 has a corresponding focusedportion, namely 281 a to 281 c. After capturing the raw OCT dataassociated with the image 280 a, the imaging system 10 can move thescanning head 84 closer to the sample surface 279 to capture the raw OCTdata for the image 280 b. As can be seen in images 280 a and 280 b, therespective focused portions 281 a and 281 b corresponds to a differentvertical region along a depth of the sample. Similarly, raw OCT data forthe image 280 c can be captured by moving the scanning head 84 closer tothe sample surface 279. By adjusting the scanning head 84 to differentvertical positions with respect to the sample, the imaging system 10 canprovide focused portions, namely 281 a to 281 c, for each verticalportion of the sample.

In some embodiments, the imaging system 10 can identify an initial scanposition on the surface map that corresponds to a highest point of thesurface of the sample and a final scan position on the surface map thatcorresponds to a lowest point of the surface of the sample. The imagingsystem 10 can adjust the position of the scanning head 84 so that eachset of vertical OCT images corresponds to the raw OCT data that iscaptured within the range between the initial scan position and thefinal scan position, and so that there is at least one OCT image witheach of the initial scan position and the final scan position at theupper edge of the imaging window.

The imaging system 10 may also determine a difference between theinitial scan position and the final scan position of the surface of thesample to estimate the amount of raw OCT data that may be required forgenerating the wide field OCT image for the sample. As will be describedwith reference to FIGS. 8E and 8F, different types of surfaces mayrequire a substantial difference in the amount of raw OCT data that isacquired in order to image provide a wide field image of the sample.

FIG. 8E shows a series 320 of OCT images for a sample surface 321 andFIG. 8F shows a series 322 of OCT images for a sample surface 323.Generally, if the difference between the initial scan position and thefinal scan position of the surface of the sample is fairly small, suchas for the sample surface 321 in FIG. 8E, the imaging system 10 candetermine that the surface of the sample is fairly flat and therefore,the imaging system 10 would not be required to capture data at too manydifferent depths. As shown in FIG. 8E, the sample surface 321 can becaptured with three horizontal OCT images 324, 326 and 328.

However, if the difference between the initial scan position and thefinal scan position of the surface of the sample is fairly large, suchas in the case of the sample surface 323 in FIG. 8F, the imaging system10 can determine that the surface of the sample is fairly uneven and theimaging system 10 would likely need to capture data corresponding to agreater number of heights to account for the unevenness of the surface.As shown in FIG. 8F, the sample surface 323 can be captured with threevertical sets of images, namely vertical image sets 330, 332 and 334.Each vertical image set 330, 332 and 334 includes more than one OCTimage for different vertical regions of the sample. The vertical imageset 330 includes images 330 a to 330 c, the vertical image set 332includes images 332 a to 332 c and the vertical image set 334 includesimages 334 a and 334 b.

It will be understood that, as described with reference to FIG. 8D, eachof the images in FIGS. 8E and 8F may be a composite image formed fromone or more images with a different vertical portion of the sample beingthe focused portion.

The focal point positioning technique described herein generally allowsfor, at least, the image quality of each of the OCT images of the sampleto be equal throughout the imaging window; non-uniform surfaces to bescanned with no or insignificant degradation of image quality; and theimaging window to be extended along the Z-axis direction towards thesample to capture greater depth information.

Referring still to FIG. 7B, at 212, the imaging system 10 then recordsthe new position of the scanning head 84, namely the new “Z” coordinate.At 214, the imaging system records the raw OCT data of the region ofinterest at a new scanning distance between the scanning head 84 and thesurface of the sample.

At 216, the imaging system 10 determines whether a full extent of thatportion of the sample has been captured. As noted, the imaging system 10may, based on the surface map or from a visual review of the sample byan operator of the imaging system 10, determine whether additional rawOCT data is required at that portion of the sample. If the imagingsystem 10 determines that additional raw OCT data is required at thatportion of the sample, the imaging system 10 repeats 210 to continue tocapture the raw OCT data at a different vertical location of the sample.However, if the imaging system 10 determines that additional raw OCTdata is not required at that portion of the sample, the imaging system10 moves the scanning head 84 to a new horizontal region of the sample(e.g., a new X, Y coordinate of the sample) at 218. The portion of thesample may be a pre-set area.

At 220, the imaging system 10 determines whether sufficient raw OCT datahas been captured for the region of interest. Again, depending on thesurface map or from a visual review of the sample by an operator of theimaging system 10, it can be determined if the imaging system 10 hascaptured sufficient raw OCT data for the entire region of interest. Theregion of interest may be a pre-set area of a surface of the sample.

If the imaging system 10 determines that sufficient raw OCT data hasbeen captured, the imaging system 10 proceeds to process the acquiredraw OCT data of the sample at any one of 184 or 186 of FIG. 7A. If theimaging system 10 determines that there is an insufficient amount of rawOCT data for the region of interest (that is, there is an insufficientamount of raw OCT data for the sample), the imaging system 10 continuesto capture additional raw OCT data by repeating 204 to 220 untilsufficient raw OCT data has been captured for the region of interest.

After acquiring the raw OCT data of the sample, the imaging system 10can generate OCT images based on the acquired raw OCT data. To generatethe OCT images, the imaging system 10 may process the acquired raw OCTdata using standard OCT reconstruction routines. For example, theimaging system 10 may process the raw OCT data acquired for everyindividual A-scan captured for the sample.

Referring again to FIG. 7A, at 184, the imaging system 10 can detect asurface for each of the OCT images being processed.

Once the imaging system 10 has acquired the raw OCT data and generatedOCT images based on the raw OCT data for the sample, the imaging system10 may process each OCT image to identify a surface position for eachgenerated OCT image. The identification of the surface position canfacilitate other image processing techniques that the imaging system 10may apply to the generated OCT images. For example, the imaging system10 can check for wrap-around artifacts in the generated OCT image basedon the detected surface position and mitigate this risk by reversing adirection of the scanning head 84 upon detection of the wrap-around. Itshould be noted that the reference frame or coordinate system does notneed to be transformed since the coordinate system is the same for dataacquisition at 182 and surface detection at 184.

Referring now to FIG. 7C, shown therein is a flowchart of an exampleembodiment of a surface detection method 184 for the OCT images.

At 230 of the surface detection method 184, a maximum intensity valuefor each OCT image can be determined.

In some embodiments, the imaging system 10 may first overlay a series ofOCT images based on the recorded height to generate a composite OCTimage. As noted, the recorded height generally corresponds to an axialdistance between the scanning head 84 and the surface of the sample. Theimaging system 10 may then determine an average for the intensity valuesof each A-scan in the composite OCT image. Since the wrap-aroundartifacts are in the opposite direction as the OCT image data, the dataassociated with the wrap-around artifacts do not cumulatively add up inthe averaged composite OCT image. The imaging system 10 may furtherapply a Gaussian filter to the averaged composite OCT image to reducenoise.

At 232, the imaging system 10 can register a depth positioncorresponding to the determined maximum intensity value as the surfacefor that OCT image.

The intensity of the signal at the surface of the sample is typicallythe highest for the sample. Therefore, the imaging system 10 canidentify the surface position for the composite OCT image based on thedetermined maximum intensity value for each A-scan.

At 234 of the method 184, it is determined whether the surface of all ofthe OCT images has been detected and recorded.

If it is determined that the surface positions of all the OCT imageshave been registered, the imaging system 10 can proceed to combine theOCT images to create a composite image (at 186 of FIG. 7A). In someembodiments, once a surface position has been recorded for the OCTimage, the imaging system 10 may further process the OCT image to clearthe data associated with the pixels that are beyond the full viewingwindow. The imaging system 10 may clear the pixels outside the surfaceposition by setting an intensity value of the corresponding pixels tozero, for example. This is one manner of eliminating any wrap-aroundartifacts that may exist in the OCT images.

If it is determined that not all of the surface positions of the OCTimages have been registered, the method 184 can continue to determinethe maximum intensity value for the remaining OCT images at 230.

Referring again to FIG. 7A, at 186, the imaging system 10 can combinethe OCT images to create a wide field OCT image for a portion of thesample.

As briefly described, combining the OCT images may involve variousdifferent image processing techniques. Some techniques, in accordancewith the teachings herein, will be described with simultaneous referenceto FIGS. 7D to 7F, and 9A to 11F.

Referring now to FIGS. 9A to 9C, generally illustrated therein is anexample combination of a series of OCT images for creating a wide fieldOCT image in accordance with the teachings herein. FIGS. 9A to 9Cgenerally illustrate the results of various image processing techniquesapplied to a set of OCT images for generating a composite image. FIG. 9Aillustrates a vertical OCT image set 282 including OCT images 282 a, 282b, 282 c and 282 d. Each of the OCT images 282 a, 282 b, 282 c and 282 dis associated with the same position of the sample (that is, the sameX,Y coordinate of the sample) but a different axial position of thescanning head 84. The focus of each of the OCT images 282 a, 282 b, 282c and 282 d is constant. FIG. 9B is an initial vertical composite image286 with each of the OCT images 282 a, 282 b, 282 c and 282 d combinedtogether and is shown for example purposes. FIG. 9C is a processedvertical composite image 288 in which some processing techniques havebeen applied to the vertical OCT image set 282 to remove some of thenoise seen in the initial vertical composite image 286.

In order to generate the initial vertical composite image 286 of FIG.9B, the imaging system 10 combines each of the OCT images 282 a, 282 b,282 c and 282 d by applying a vertical stitching process. An examplevertical stitching process will be described with reference to FIGS. 7Dand 11A to 11F. An example horizontal stitching process will bedescribed with reference to FIGS. 7E and 10D to 10F.

Generally, the stitching methods in accordance with the teachings hereincan be used to generate wide field 2D and 3D OCT images. The stitchingmethods described herein may also be used to combine OCT images that areacquired from non-uniform surfaces and equalize the image qualitythroughout the entire imaging window. As a result, the imaging system 10can scan non-uniform surfaces without any degradation of image qualityand the imaging system 10 can also extend the imaging window along theZ-axis further into the sample to capture greater depth information. Itwill also be noted that when used with the focal point positioningmethod in accordance with the teachings herein, the stitching methodsdescribed herein may also improve signal strength of the interferencesignal, and increase image quality and resolution at penetration depthsof up to 2 mm beneath the surface of the sample. Accordingly, theimaging system 10 may employ the stitching methods described herein toscan through a range of depths to improve signal strength of theinterference signal at all attainable depths.

It will be appreciated that the imaging system 10 may not be required toconduct both vertical and horizontal stitching methods when combiningOCT images (as illustrated in the example shown in FIGS. 8E and 8F).When imaging small regions of interest, the imaging system 10 may onlyneed to conduct vertical stitching in order to generate the wide fieldOCT image. On the other hand, the imaging system 10 may apply only thehorizontal stitching method when imaging flat specimens or phantoms.

Generally, the vertical stitching method involves overlaying the OCTimages based on associated spatial information (e.g., recorded heightoffsets), and fine-tuning an alignment of the OCT images. For two OCTimages, the imaging system 10 may fine tune the alignment between thetwo OCT images by vertically shifting the alignment over a number ofiterations and measuring the entropy associated with the overlap regionof the two OCT images. The imaging system 10 can continue to repeat thealignment process for each OCT image. In some embodiments, the imagingsystem 10 may apply a weighted average to at least the overlap regionbetween two OCT images in order to blend the overlap in order to furtherreduce horizontal step lines in the final combined OCT image and tofurther improve image quality.

Referring now to FIG. 7D, shown therein is a flowchart of an exampleembodiment of a vertical stitching method 188. For ease of exposition,the example vertical stitching method 188 will be described withreference to the vertical OCT image set 282 of FIG. 9A.

At 240, the imaging system 10 receives a vertical OCT image set, such asthe vertical OCT image set 282 of FIG. 9A. The spatial informationassociated with each of the OCT images in the vertical OCT image set isalso received by the imaging system 10 so that the imaging system 10 canalign the OCT images accordingly.

At 242, the imaging system 10 overlays the images in the vertical imageset. When overlaying an OCT image with a portion of a neighbouring OCTimage, the imaging system 10 can determine entropy associated with theoverlap region between the OCT image and the neighbouring OCT image inorder to improve the alignment of the OCT image with the neighbouringOCT image. An example alignment process with now be described withreference to FIGS. 11A to 11E.

FIGS. 11A to 11E are various overlaid OCT images with different offsetdistances in accordance with an example embodiment. In this case, theoffset is a height offset so the images are shifted vertically, eitherupwards or downwards, with respect to one another. For the purpose ofconsistency, the overlaid OCT images in FIGS. 11A to 11E correspond tothe OCT images 282 a and 282 b of FIG. 9A. In this example, the OCTimage 282 b is the neighbouring OCT image of the OCT image 282 a. Inorder to properly align the OCT images 282 a and 282 b together, theimaging system 10 can generate one or more intermediary composite imageswith a different amount of overlap, or offset distance, between the OCTimages 282 a and 282 b. The offset distance may be in terms of pixels orany other suitable measurement. In some embodiments, the imaging system10 may also determine an average for the overlapped region so that theintensity values at the overlapped region is not doubled.

FIG. 11A shows an intermediary composite image 312 a with an offsetdistance of 150 pixels, FIG. 11B shows an intermediary composite image312 b with an offset distance of 160 pixels, FIG. 11C shows anintermediary composite image 312 c with an offset distance of 170pixels, FIG. 11D shows an intermediary composite image 312 d with anoffset distance of 180 pixels, and FIG. 11E shows an intermediarycomposite image 321 e with an offset distance of 190 pixels. It will beunderstood that intermediary composite images can be generated for moreoffset distances and that only five offset distances are shown withFIGS. 11A to 11E for ease of exposition.

The imaging system 10 can then determine entropy for the overlap regionof each of the intermediary composite images 312. Specifically, theimaging system 10 can measure the entropy associated with the overlapbetween the OCT images 282 a and 282 b in each of the intermediarycomposite images 312 a to 312 e. One example method to determine entropywill now be described. However, it will be understood that differentmethods of determining entropy are available and may be used instead ofthe technique described herein.

As is known in the art, entropy (H) is a statistical measure ofrandomness. The entropy of each of the intermediary composite images canbe determined by measuring the standard deviation at the overlap in theintermediary composite images. In some embodiments, the entropy for anOCT image described herein can be defined using equation (1) below:H=−Σ _(k=0) ^(M-1) p _(k) log₂(p _(k))  (1)where M is a number of gray levels of the OCT image (there are 65536bins for 16 bit unsigned integers) and p_(k) is the probabilityassociated with a gray level k. The probability of each gray level, k,can be determined by creating a histogram of the gray level imageintensity values. The number of bins in the histogram is equal to M. Theprobability of each gray level, k, can be calculated using equation (2)below:

$\begin{matrix}{p_{k} = \frac{n_{k}}{\sum n}} & (2)\end{matrix}$where n_(k) is a count for gray level k and Σn is a total number of thecounts for the gray levels.

Continuing with the example of FIGS. 11A to 11E, the imaging system 10can identify which offset distance is most appropriate for aligning theOCT images 282 a and 282 b based on the entropy determined for eachintermediary composite images 312 a to 312 e. Generally, the entropy foran image can be used to characterize the texture of that image. A lowerentropy value can generally indicate that there is a greater order for agiven overlap between the OCT images 282 a and 282 b and a higherentropy value can indicate that there is less order. Less orderindicates that there is greater noise at the overlap between the OCTimages 282 a and 282 b. The higher entropy value can indicate that theOCT images 282 a and 282 b are poorly aligned. The imaging system 10,therefore, identifies the intermediary composite image associated withthe lowest entropy value as the composite image for the OCT images 282 aand 282 b. The imaging system 10 may also record the amount of overlapfor that identified intermediary composite image for later use.

FIG. 11F is a plot 316 of the entropy associated with the intermediarycomposite images associated with the OCT images 282 a and 282 b atdifferent offsets. As shown in the plot 316, the offset distance that isassociated with the lowest entropy value is approximately 172 pixels.The imaging system 10 can therefore use the offset distance of 172pixels for aligning the OCT images 282 a and 282 b to generate thecomposite image for the OCT images 282 a and 282 b.

The imaging system 10 can continue to overlay images from the verticalOCT image set 282 using the composite image generated for the OCT images282 a and 282 b as an intermediary composite image. That is, the imagingsystem 10 can now overlap the OCT image 282 c with the intermediarycomposite image generated for the OCT images 282 a and 282 b accordingto an offset as determined based on the minimum entropy techniquedescribed above to form another intermediary composite image. Ingeneral, the imaging system 10 can continue to overlay the images in thevertical OCT image set 282 to generate an initial vertical compositeimage 286 until the initial vertical composite image 286 fills an entireimaging window and/or until all the images in the vertical OCT image set282 have been used.

In some embodiments, the image quality of the initial composite imagecan be improved. At 246, for example, focused portions for each OCTimage in the vertical image set are identified. This may involve usingthe surface position determined individually for each OCT image toidentify regions of the sample that are in focus. As described, thefocused region for each OCT image is approximately the region locatedbelow the upper edge of the imaging window by a distance thatcorresponds to the scan adjustment distance. In some embodiments, thefocused portions can correspond to a center portion of the imagingwindow for that OCT image.

Referring again to FIG. 9A, as shown, each of the OCT images 282 a, 282b, 282 c and 282 d is associated with a corresponding focused portion284 a, 284 b, 284 c and 284 d, respectively. The focused portiongenerally includes minimal noise and minimal artifacts.

At 248, only the focused portions of each of the OCT images are used toform the initial vertical composite image 286. For example, the imagingsystem 10 can remove the wraparound artifacts in each of the OCT images282 a, 282 b, 282 c and 282 d by retaining only the respective focusedportions 284 a, 284 b, 284 c and 284 d. The imaging system 10 may alsoremove any signal associated with the medium external to the surface ofthe sample based on the surface information previously registered forthe OCT images 282 a, 282 b, 282 c and 282 d (in other words remove thesignal outside of the surfaces for each of OCT images 282 a, 282 b, 282c and 282 d).

At 250, the imaging system 10 generates a processed vertical compositeimage, such as the processed vertical composite image 288 of FIG. 9C byonly using the focused portions 284 a, 284 b, 284 c and 284 d of each ofthe OCT images 282 a, 282 b, 282 c and 282 d after removing the signaloutside of the each of the surfaces in the OCT images 282 a, 282 b, 282c and 282 d (in an alternative embodiment, this removal step may not bedone). The processed vertical composite image 288 is formed by shiftingthe focused portions 284 a, 284 b, 284 c and 284 d with respect to oneanother based on the offsets determined at 242 of method 188 to aligneach of the shifted focused portions 284 a, 284 b, 284 c and 284 d witheach other.

Referring now to FIGS. 10A to 10C, shown therein is an examplecombination of another series of OCT images for creating a wide fieldOCT image. Similar to FIG. 9A, FIG. 10A illustrates a vertical OCT imageset 292 including OCT images 292 a, 292 b, and 292 c. Each of the OCTimages 292 a, 292 b, and 292 c is associated with the same position ofthe sample (that is, same X,Y coordinate of the sample) but a differentaxial or vertical position of the scanning head 84 with respect to thesurface of the sample being imaged. The focus for each of the OCT images292 a, 292 b, and 292 c is constant. Each of the OCT images 292 a, 292b, and 292 c is also associated with a respective focused portion 294 a,294 b and 294 c.

FIG. 10B is an initial vertical composite image 296 with each of the OCTimages 292 a, 292 b, and 292 c combined together based on the offsets asdetermined by act 242 of method 188. The vertical stitching method asdescribed with reference to FIG. 7D may be used for combining the OCTimages 292 a, 292 b, and 292 c.

Similar to FIG. 9C, FIG. 10C is a processed vertical composite image298. The imaging system 10 can generate the processed vertical compositeimage 298 by retaining only the focused portions, namely focused portion294 a, 294 b and 294 c, of the OCT images 292 a, 292 b, and 292 c. Theimaging system 10 can also remove any signal associated with the mediumexternal to the surface of the sample based on the surface positionpreviously registered for the OCT images 292 a, 292 b, and 292 c.

After applying the vertical stitching method to each of the verticalimage sets, the imaging system 10 can apply a horizontal stitching tothe processed vertical composite images. It should be again noted thatthe imaging system 10 can apply the horizontal stitching method to OCTimages that were not previously vertically stitched together.

Generally, the horizontal stitching method can involve overlaying theOCT images based on associated spatial information (e.g., recordedpositions along the surface of the sample) in a horizontal fashion. Thespatial information may be the position data (e.g., X,Y coordinates)associated with the scanning head 84 as it moves horizontally along thesurface of the sample when acquiring raw OCT data. Similar to thevertical stitching method described with reference to FIG. 7D, theimaging system 10 can continue to fine tune the overlap in the overlaidimages by minimizing the entropy associated with the overlap between thehorizontally overlaid images. In some embodiments, the imaging system 10may further apply a weighted filter to the overlaid image to blend theoverlap and further reduce noise at the overlap region. The imagingsystem 10 can continue to combine another neighbouring OCT image to theoverlaid image and so on and so forth until all of the horizontal imageshave been combined or until the imaging window has been filled.Therefore, a series of OCT scans over the sample can be horizontallyand/or vertically stitched to generate a composite macro view of aregion of interest of the sample. This region of interest may be a smallor a large portion of the sample.

Referring now to FIG. 7E, shown therein is a flowchart of an exampleembodiment of a horizontal stitching method 190. For ease of exposition,the example embodiment of the horizontal stitching method 190 will bedescribed for the processed vertical composite images 288 and 298 of therespective FIGS. 9C and 10C with reference to FIGS. 10D to 10F. FIGS.10D to 10F show an example combination of OCT images for generating awide field OCT image.

At 260, the imaging system 10 can receive a horizontal image set alongwith associated position information. As described with reference toFIG. 7B, the position information may be associated with the position ofthe scanning head 84 as it captures the raw OCT data. The positioninformation for each OCT image in the horizontal image set will includeat least the X, Y coordinate.

In the example shown in FIGS. 10D to 10F, the horizontal image set 302includes, at least, the processed vertical composite image 288 and theprocessed vertical composite image 298.

At 262, the imaging system 10 can overlay two images in the horizontalimage set 302. Referring still to FIG. 10D, the processed verticalcomposite image 288 can be overlaid with the processed verticalcomposite image 298 to generate an initial horizontal composite image304, as shown in FIG. 10E. To properly overlay the OCT images in thehorizontal image set 302, the imaging system 10 may determine entropyassociated with the overlap, as described with reference to FIGS. 7D and11A to 11F, and select the offset distance associated with the lowestentropy value as the proper alignment for the processed verticalcomposite images 288 and 298.

Accordingly, the imaging system 10 may fine tune the alignment betweenthe processed vertical composite images 288 and 298 by verticallyshifting (if necessary) at 263 and horizontally shifting the images 288and 298 at 264 over a number of iterations and measuring the entropyassociated with the overlap of the processed vertical composite images288 and 298. In some embodiments, such as the example shown in FIG. 10D,the processed vertical composite images 288 and 298 may have a verticaloffset from each other that requires vertical stitching (i.e., based onthe process described with reference to FIG. 7D) prior to horizontalstitching. Once the processed vertical composite images 288 and 298 arevertically aligned, the imaging system can horizontally align the images288 and 298.

The imaging system 10 can continue to repeat the alignment process foreach remaining OCT image in the horizontal image set 302.

At 266, the imaging system 10 determines whether all the images in thehorizontal image set 302 have been stitched together. If the imagingsystem 10 determines that there are images in the horizontal image set302 that have not been included into the initial horizontal compositeimage 304, the imaging system 10 returns to 262. If the imaging system10 determines that all of the images in the horizontal image set 302have been combined into the initial horizontal composite image 304, theimaging system 10 can perform further processing on the horizontal imageset to generate a processed horizontal composite image (at 268), such asthe processed horizontal composite image 306 shown in FIG. 10F.

As described, when combining the OCT images together, the imaging system10 may also process the OCT images to remove artifacts. An exampleartifact is a saturation artifact which appears as streaks in the OCTimage.

The saturation artifact is generally associated with a noise signalmeasured at a region outside the surface of the sample that exceeds asignal in air. The signal in air is an expected baseline signal for theregion external to the surface of the sample. Generally, when obtainingraw OCT data, the scanning head 84 can become saturated when too muchlight is reflected back from the surface of the sample. The result ofthe saturation is a vertical streak through the OCT images and throughthe entire imaging window. To minimize the saturation artifact in theOCT images, the imaging system 10 can normalize the noise signal withreference to the signal in air.

Referring now to FIG. 7F, shown therein is a flowchart of an examplemethod 192 for minimizing saturation artifacts.

At 270, the imaging system 10 can identify an external region based on asurface position of the OCT image. The external region borders theexterior of the surface of the sample. The imaging system 10 maydetermine the surface position based on the surface detection methoddescribed with reference to FIG. 7D, for example.

At 272, the imaging system 10 can detect a noise signal in the externalregion of the OCT image.

For example, an OCT image can be represented as a gray level image of 16bit unsigned integers and u(x,y) can represent the grey level, orbrightness, at the point (x,y) on the image. Therefore, noise in an areaoutside the region of the sample can be defined with the equation (4) asfollows:N _(x) =u(x,y )_(window)  (4)where the y-values for the gray levels are averaged.

At 274, the imaging system 10 can determine whether an intensity of thenoise signal exceeds an intensity threshold.

To reduce the saturation artifact, the noise signal can be analyzed.Generally, a certain amount of noise is expected in the external region.However, significant deviations from the signal in air may substantiallyimpair the quality of the OCT image. Accordingly, if the imaging system10 determines that the detected noise signal exceeds the intensitythreshold, the imaging system 10 may need to process the OCT image inorder to reduce the noise signal to minimize the saturation artifacts.

The intensity threshold may vary depending on the imaging window andleveling. Once the window and level has been set for an image set, thebaseline signal can be expressed as b_(air)=ū_(air). In someembodiments, the intensity threshold, T_(air), may be at least threetimes the standard deviation of the expected signal in air, as expressedby equation (5) below;T _(air)=3*(σ_(ū) _(air) )  (5)where σ_(ū) _(air) represents the standard deviation of the expectedsignal in air.

At 276, the imaging system 10 can adjust characteristics of the OCTimage if the intensity of the noise signal exceeds the intensitythreshold. For example, the imaging system 10 may normalize the A-scandata corresponding to the OCT image with respect to the determined noisesignal intensity to reduce the saturation artifacts. That is, the A-scandata can be normalized using equation (6) as follows:

$\begin{matrix}{{u^{\prime}\left( {x,y} \right)} = {\frac{u\left( {x,y} \right)}{N_{x}}\left( b_{air} \right)}} & (6)\end{matrix}$By detecting the noise signal and adjusting the characteristics of theOCT image if the noise signal intensity exceeds the intensity threshold,the imaging system 10 can generate a high resolution OCT image withminimal artifacts.

In at least one embodiment of the imaging system 10, signalpre-processing may include techniques such as low pass filtering,rolling averages and nearest neighbor corrections to reduce the effectof outliers and holes in the acquired raw OCT data.

In at least one embodiment of the imaging system 10, using the surfacemapper data, a path planning processing pipeline, based on a set ofpre-programmed parameters, will know the midpoint of a selected area,how many c-scan areas to divide it into, and the slope of each region.With this information, the imaging system 10 can use the midpoints ofeach C-scan area as the location where the OCT probe will be placed andthe imaging system 10 can get a rough idea about how the height changesover the area with the area dictating how many incremented depth scansto perform to penetrate equally over the entire surface or a portion ofthe surface of the sample as desired.

It should be noted that due to the various angles and depressions, thesurface of the tissue specimen might skew the digital interpretation ofthe surface. Surface mapping can be modified to address this problem byhaving the operator place the sample's region of interest as normal tothe OCT beam as possible. To facilitate this, instead of there being apre-scan from the surface mapper, a square can be projected onto thesample with a low power light source. The operator, such as a surgeonfor example, can then place the region of interest within that squareensuring that it is as flat as possible. The scanner can then scan thetissue specimen as it would have before.

Various embodiments are described herein that may be used to create widefield OCT images through appropriate combination of smaller, higherresolution OCT images through the use of guided mechanical movement andimage alignment algorithms.

The various embodiments described herein may also facilitate clear anderror-free communication of tissue orientation from tissue resection (bya surgeon, for example) for tissue analysis (by a pathologist, forexample).

The various embodiments described herein may also minimally disrupt theworkflow in the operating room by maintaining sterility, tissueintegrity and orientation information of resected tissue samples.

With regards to assessing tumor margin widths, OCT imaging may be usedto reduce the prevalence of repeat surgeries since it may provide afaster and more accurate intraoperative tool for assessing margin widthswhile a surgery is ongoing. For example, an OCT-based intraoperativeimaging system may be used to provide near real-time imaging informationabout the internal structure of tissue samples excised during breastconserving surgery. The results of preclinical and initial clinicalstudies conducted with a prototype wide-field OCT imaging system arepresented in further detail below with respect to FIGS. 12A to 14B.

Furthermore, OCT image processing methods may be implemented by anOCT-based intraoperative imaging system, or any other suitable imageprocessing system, to provide additional information to assist inassessing tumor margin widths. For example, OCT image processing methodsaccording to the teachings herein can be used to assist a surgeon indetermining whether an actionable boundary exists in an excised tissuesample. An “actionable boundary” indicates that two regions above andbelow a defined boundary are sufficiently distinct, and that theboundary is within a defined depth from the surface. The various methodsaccording to the teachings herein define a way to determine this levelof difference to which may be used to assist a surgeon to determinewhether sufficiently enough tissue has been removed (i.e. to remove atumor).

When excising cancerous tissue a surgeon may want to have a continuouslayer of healthy tissue surrounding the excised tumor. The healthy layerof tissue surrounding the excised tumor is referred to as the margin.The narrowest point of the margin is referred to herein as the marginwidth. A surgeon may prefer a larger margin to ensure that no residualcancer or other tumor is left in the patient after surgery. However,larger margins may come at the cost of cosmetic effects since moretissue than necessary is removed.

Furthermore, an acceptable margin width may change from surgeon tosurgeon. For example, an acceptable margin width could be 1 mm or 2 mmdepending on the surgeon. If the excised tissue does not have anacceptable margin width, additional tissue may have to be removed from apatient to ensure sufficient margin width.

If a boundary between non-tumor tissue and tumor tissue is detected thatis less than an acceptable margin from the surface of the tissue, an“actionable boundary” is said to be detected. If it is actionable, thesurgeon may choose to excise further tissue based on the information ifmore tissue is available. In some cases, there may be no more tissueavailable to be excised, for example where the tumor is close to theskin or where the tumor is close to the chest wall. There may also beother situations which prevent the surgeon from excising further tissueas is known to those skilled in the art.

Various OCT image processing methods are described herein that may beused to assist a surgeon in at least one of identifying if a boundaryexists, indicating the depth of the boundary at various points in thetissue sample, determining the degree of difference between tissues oneither side of the boundary, and determining whether an actionableboundary is detected. Some example embodiments of OCT image processingmethods, which may be used for tissue assessment, are presented infurther detail below with respect to FIGS. 15 to 22F. In someembodiments, the imaging system 10 may be configured to perform the OCTimage processing methods for implement tissue assessment according tothe teachings herein. In other embodiments, other computing devices,having similar hardware components needed for image processing (as shownin FIG. 5), may be used to perform these OCT image processing methodsfor tissue assessment.

While developing a wide field OCT imaging prototype for the imagingsystem 10, pre-clinical performance studies were first carried out intissue-mimicking phantoms and in ex-vivo normal and tumor tissues from arat model (see FIGS. 12A to 12D). This enabled the imaging prototype tobe tested and improved as well as for comparisons to be made between theresults from the reconstructed OCT images and from histopathology.

FIG. 12A shows an image of a histopathology sample 400 of a rat ovarytumor that is derived from a human breast cancer cell line (MT-1)xenograft. Normal tissue 405 and malignant tissue 410 can bedistinguished in the histopathology sample 400.

FIG. 12B shows a reconstructed OCT image 415 corresponding to thehistopathology sample 400. The normal tissue region 405 and themalignant tissue region 410 can be also identified in the reconstructedOCT image 415.

FIG. 12C shows an image of a second histopathology sample 430 of a ratovary tumor that is derived from a human breast cancer cell line (MT-1)xenograft. Normal tissue 435 and malignant tissue 440 can bedistinguished in the histopathology sample 430.

FIG. 12D shows a reconstructed OCT image 445 corresponding to the secondhistopathology sample 430. The normal tissue region 435 and themalignant tissue region 440 can also be identified in the reconstructedOCT image 445.

The results shown in FIGS. 12A to 12D are an example of the generalfindings in tissues that different layers of tissue and different typesof tissue (e.g. adipose, connective, cancerous) can be identified in theOCT images and that tissue boundaries can be clearly defined andlocalized.

Referring now to FIGS. 13A to 14B, initial images were acquired offreshly excised human breast lumpectomy specimens. For this, theprototype imaging system was placed in the pathology lab and specimenswere scanned during the cold ischemic time between excision and standardpathological processing.

FIG. 13A shows an image of a freshly excised breast lumpectomy specimen500. The lumpectomy specimen 500 has a superior section 505, a posteriorsection 510 and an inferior section 515. An OCT image of the lumpectomyspecimen 500 was obtained using the prototype imaging system over aB-scan path 520.

FIG. 13B shows a reconstructed OCT image 525 over the B-scan path 520 ofthe lumpectomy specimen 500. The superior section 505, posterior section510 and inferior section 515 can be identified in the reconstructed OCTimage 525.

Different types of tissue can be identified in reconstructed OCT images.For example, a layer of connective tissue 530 and a layer of adiposetissue 535 are identified in the reconstructed OCT image 525. Withoutany assistance or automated tools, the identification of tissue layersby a user on an OCT image may require specialized training and there maystill be inaccurate results.

FIG. 14A shows an image of a second freshly excised breast lumpectomyspecimen 550. The lumpectomy specimen 550 has a superior section 555, aposterior section 560, an inferior section 565, a medial section 570 anda lateral section 575. An OCT image of the lumpectomy specimen 550 wasobtained using the prototype imaging system over B-scan path 580.

FIG. 14B shows a reconstructed OCT image 585 over the B-scan path 580 ofthe lumpectomy specimen 550. The posterior section 560, the medialsection 570 and the lateral section 575 can be identified in thereconstructed OCT image 585.

OCT images can be used to identify when narrow margins exist in excisedtissue. Narrow margins may exist when tumor tissue is present in anexcised tissue sample and is closer to the surface of the excised tissuesample than is desired by a surgeon. A narrow margin that is less than adesired margin is indicative of an “actionable boundary”. Often, amargin less than 2 mm is considered to be indicative of an “actionableboundary”; however acceptable margin width may differ depending on thesurgeon.

In the OCT image 585, a narrow margin 590 has been identified. Theidentification of the narrow margin 590 in the OCT image 585 indicatesthat tumor tissue is present in the excised tissue sample and may becloser to the surface of the lumpectomy specimen 550 than is desired bya surgeon. This indicates that additional tissue may be excised.

The preliminary images seen in FIGS. 13A to 14B appear to demonstratethe capability of wide field OCT imaging to provide high-resolution,high-contrast subsurface images from within breast lumpectomy specimensthat can be used to detect actionable boundaries.

In particular, the experimental work appears to demonstrate that: a)preclinical OCT images can be well correlated to standard pathologyassessments, b) OCT provides images in which certain different tissuetypes can be distinguished from one another, and c) OCT is capable ofproviding subsurface images of human breast lumpectomy specimens thatcan be used to detect and localize the boundaries between varioustissues in these specimens. The experimental results suggest that OCTimaging may be useful as a near-real time surgical assessment tool. Thiscapability may be extended by using wide field OCT imaging as describedherein which can be used to provide an increased field of view of atissue sample.

Accordingly, in some embodiments of an OCT imaging system, a tissueassessment method may be used on OCT images that may or may not be widefield OCT images. Once an OCT image has been reconstructed, the tissueassessment method may be used to quantitatively measure the degree ofdifference between a region above and a region below an identified orhighlighted boundary in the reconstructed OCT image by using variousmeasures, such as but not limited to the optical characteristics of thesample. The tissue assessment method may then indicate whether aboundary is “actionable” or “non-actionable”.

In some cases, a patient may be injected with a contrast agent prior tohaving a tissue sample excised. In some cases, a contrast agent may beapplied to the tissue sample after it has been excised. A contrast agentmay cause cancer cells to emit fluorescence. The use of fluorescence inconjunction along with at least one of the tissue assessment methodsdescribed herein may further improve the speed of assessing tumor marginwidths.

Fluorescence may direct a user to a portion of a tissue sample wherecancerous tissue is expected. In some cases, a user may only perform OCTimaging methods on that portion of the tissue sample near where thecancerous tissue is expected. In some cases, a user may also use thefluorescence after an OCT image has been reconstructed to selectportions or regions of the OCT image to focus on in their review. Forexample, a user may only review those B-scan images or portions ofB-scan images where fluorescence was detected. Similarly, regionsindicated by fluorescence may be used to limit the portion or window ofthe OCT image for which a tissue assessment method is performed.Reducing the portion of the tissue sample for which OCT imaging methodsand tissue assessment methods are performed may provide even fasterintraoperative results.

Referring now to FIGS. 15A to 15C, shown therein is an example of aB-scan image 600 of an OCT image at various stages of processing by anexample embodiment of a tissue assessment method in accordance with theteachings herein. As used herein, the term “B-scan image” refers to areconstructed 2-D image based on raw OCT data collected for a 2-D sliceof a tissue sample during OCT imaging. As noted, in computed tomography,it is generally understood that a B-scan is a 2D slice through thesample and a C-scan is a typical 3D visualization of a volume of thesample. The terms “C-scan image”, “A-scan image” and 3D-OCT image usedherein can be interpreted in a likewise manner as the term “B-scanimage” in terms of being reconstructed from raw OCT data.

In an OCT imaging system, the raw OCT data is typically collected asinterferometric data. The interferometric data can be detected, forexample, as the number-of-photons for a given frequency. The number ofphotons for each given frequency can be recorded as the intensity forthat frequency. The raw OCT data therefore comprises frequency data thatcan be processed to obtain an OCT image that indicates the intensity ofthe light reflected by the tissue sample at a plurality of points. Forexample, the raw OCT data can be converted to OCT signal intensity datausing a Fourier transform at each point in space. An OCT image that isgenerated from the OCT signal intensity data may be referred to as a“reconstructed OCT image”.

Raw OCT data is collected and may be organized as a series of B-scans or2-D scans or “slices” of a tissue sample. The slices of raw OCT data maybe collected at various resolutions which determine the distance betweenadjacent B-scans of the tissue sample. For example, in some casesadjacent B-scans may be separated by distances in the tens or hundredsof micrometers.

In general, OCT image data may be 3D image data that comprises aplurality of B-scan image data. Accordingly, B-scan images represent a2-D slice of the 3D OCT image. Each B-scan image comprises a pluralityof reconstructed A-scans. Each reconstructed A-scan represents OCTsignal intensity data as a function of depth at a particular slice in aB-scan image.

The OCT image data that corresponds to a reconstructed OCT image may beobtained directly from the raw OCT data that was acquired by the imagingsystem 10 during operation. In some embodiments, the OCT image data maycomprise processed OCT image data which corresponds to the raw OCT dataafter undergoing image pre-processing such as noise reduction forexample.

FIG. 15A shows an example of B-scan image 600 with borders 610indicating regions of interest in the B-scan image 600. The borders 610may be identified based on a variety of optical characteristics of thetissue sample shown in the OCT image. In this example, the borders 610highlighted in FIG. 15A represent the outline of regions having highattenuation in the B-scan image 600.

Regions of high attenuation can be determined by applying variousprocessing techniques to the OCT image data. For example, theattenuation at a point in a tissue sample may be determined bycalculating the rate of decay of the OCT signal intensity data at apoint in the tissue sample. In some embodiments, a reconstructed A-scancan represent the OCT signal intensity data as a function of depth in atissue sample for a slice of a B-scan image. The rate of decay of theOCT signal intensity data may then be calculated from the reconstructedA-scan using suitable methods such as, but not limited to, linearregression analysis.

Similarly, other optical characteristics such as, but not limited to,fluctuations in the OCT signal intensity data may be indicative ofregions of interest. Fluctuations in the OCT signal intensity data canrepresent a measure of the texture of a tissue sample shown in an OCTimage. An example method for determining the attenuation and texture inan OCT image is discussed in more detail with regard to FIGS. 20A to20F.

In some embodiments, the regions of interest may themselves behighlighted in an OCT image. The regions of interest can be highlightedby applying a mask to the OCT image. In some embodiments, the mask maybe generated to correspond directly to the values of the opticalcharacteristics that indicate the regions of interest. For example, acolor mask can be used where the RGB value of the mask at a pixel in theOCT image is based on one or more optical characteristics of the OCTimage at that point.

In some embodiments, a mask may be generated to indicate whether valuesof optical characteristics are above certain thresholds. For example, aregion having attenuation above an attenuation threshold may have a maskapplied to that region, whereas regions where the attenuation is notabove the threshold would not have the mask applied.

FIG. 15B shows an example of the same B-scan image 600 with a boundary620 identified therein. The boundary 620 distinguishes a first region622 from a second region 624. Once the boundary 620 has been identifiedin the B-scan image 600, a tissue assessment method can analyze theregions on both sides of the boundary 620 to determine the degree ofdifference or differentiation between the regions. This degree ofdifference may then be used to determine if the boundary is actionable.

In some embodiments, boundary 620 may be identified automatically.Automatic boundary identification can be based on various opticalcharacteristics of the OCT image data. For example, in some embodiments,boundaries may be identified using the borders of regions of interest.Alternatively, in some embodiments, the optical characteristics used toidentify borders of the regions of interest may provide a coarseindication of a boundary. In some embodiments, additional opticalcharacteristics can be used to improve the selectivity of theidentification of a boundary in the OCT image.

In some embodiments, the optical characteristics used to provide acoarse indication of a boundary may be the attenuation of the OCT signalintensity data and the fluctuation of the OCT signal intensity data. Insome embodiments, the additional optical characteristics may include,but are not limited to, area under the profile of the A-scan, number ofsignal maxima in the OCT signal intensity data, mean distance betweensignal maxima in the OCT signal intensity data, standard deviation ofthe peak distance in the OCT signal intensity data, mean frequency ofthe OCT signal intensity data, and standard deviation of the frequenciesin the OCT signal intensity data, for example

In some embodiments of a tissue assessment method, a user can manuallyidentify the boundary in a reconstructed OCT image. However,highlighting regions of interest and the borders of regions of interestin a reconstructed OCT image may assist a user in selecting anappropriate boundary.

Highlighting regions of interest and the borders of regions of interestin a reconstructed OCT image may have useful applications in trainingusers that are unfamiliar with interpreting OCT images. Highlightingregions of interest using a mask on the OCT image or by identifying theborders of the region of interest, may direct users to more easilyidentify boundaries between different types of tissues.

Highlighting various optical characteristics in a reconstructed OCTimage may also assist a user in identifying the particular type oftissue or tissues that are present in a reconstructed OCT image.Different tissue types typically have different optical properties, andusing those optical properties to generate a color mask may assist auser in more easily identifying the different tissue types.

FIG. 15C shows an example of a B-scan image 600 after undergoinganalysis by an example embodiment of a tissue assessment method inaccordance with the teachings herein. In FIG. 15C, the B-scan image 600has a mask 630 displayed thereon to indicate a degree of differencebetween a first region 622 and a second region 624. The mask 630 isdisplayed on the B-scan image 600 over a window in each of the regions622 and 624 corresponding to the boundary margin width 640.

In FIG. 15C, the mask 630 is shown using cross-hatching to identify thedifferent regions 622 and 624. The difference in the cross-hatching ofthe mask 630 in the first region 622 and in the second region 624indicates that a high degree of difference has been detected betweenthese two regions. As legend 632 indicates, the mask 630 shown in FIG.15C is indicative of an actionable boundary. A tissue assessment method,in accordance with the teachings herein, may generate the mask 630 bycomparing at least one optical characteristic of the first region 622and the second region 624 on either side of the boundary 620.

It will be understood that in alternative embodiments, other types ofmasks may be used for the mask 630, such as a color mask that usesdifferent colors or a shading mask that uses different levels of shadingto indicate that the two regions have a certain degree of difference.

The boundary margin width 640 can be identified by a tissue assessmentmethod as the narrowest distance between the tissue surface position andthe boundary 620. The tissue surface position represents the exteriorsurface of the excised tissue sample being imaged. Depending on themethod of OCT scanning, the exterior surface of the excised tissuesample may be adjacent to gel, air, water, glass and the like, as isknown be those skilled in the art. With respect to OCT images orwide-field OCT images, in some embodiments the maximum value in anA-scan may be used to locate the estimated tissue surface position.

In accordance with the teachings herein, in at least one embodiment, atissue assessment method can also provide a discrimination scoreindicating the degree of difference between the first and second regionson either side of a boundary. In some embodiments, the discriminationscore may indicate the degree of difference on a continuous orincremental scale from 0 to 1 with a score of 0 representing almostidentical regions, and a score of 1 representing regions with nosimilarity. In some embodiments, the discrimination score can be abinary value indicating whether the degree of difference is greater orless than a predefined threshold.

The predefined threshold can be defined and optimized using clinicaldata from known datasets. Accordingly, the threshold may change fordifferent clinical data such as for one or more of different types ofpatients, different types of tissues or different types of tumors. Insome cases, the predefined threshold may be adjusted depending on thedensity of tissue expected in the excised tissue. For example, youngerpatients typically have denser tissue so the threshold may be adjusteddepending on the age of the patient.

In some embodiments, the margin width may be displayed on the OCT image.Furthermore, in at least some embodiments, when the tissue assessmentmethod indicates a high degree of difference between the two regions andthe margin of the tissue is less than an acceptable width, an indicationof an actionable boundary is presented to a user of the imaging system10.

Referring now to FIG. 16, shown therein is a flowchart of an exampleembodiment of a tissue assessment method 650. The tissue assessmentmethod 650 may be implemented by any suitable image processing system,such as the imaging system 10, for example.

At 652, OCT image data for an OCT image is received. In someembodiments, the OCT image can be a reconstructed 3-D OCT image or areconstructed 2-D OCT image of a tissue sample that may be reconstructedusing conventional techniques or using the various wide field OCTimaging techniques described herein.

The OCT image data can be used for various aspects of tissue assessment.For example, the OCT image data can be used to identify regions ofinterest in the OCT image, identify boundaries distinguishing tworegions in the OCT image and to determine the degree of differencebetween the regions in the OCT image.

At 654, a boundary is identified in the OCT image to distinguish betweena first region and a second region. Referring now to FIG. 17, showntherein is a flowchart of an example embodiment of a boundaryidentification method 670 for identifying a boundary in an OCT image. Itshould be understood that this is one example and there may be othertechniques that may be used for boundary identification as is known bythose skilled in the art.

At 672, values for at least one optical characteristic for a pluralityof points in the OCT image are measured by using the OCT image datacorresponding to the plurality of points in the OCT image. The opticalcharacteristics may be any optical characteristic derived from the OCTimage data such as, but not limited to the slope of the OCT signalintensity data, the standard deviation of the OCT signal intensity data,the area under the profile of the A-scan, the number of signal maxima inthe OCT signal intensity data, the mean distance between signal maximain the OCT signal intensity data, the standard deviation of the peakdistance in the OCT signal intensity data, the mean frequency of the OCTsignal intensity data, and the standard deviation of the frequencies inthe OCT signal intensity data, for example.

At 674, the measured values for the optical characteristics at eachpoint in the plurality of points of the OCT image are compared tothreshold values. In some embodiments, the threshold values may beadjusted by a user to adjust the sensitivity for identifying regions ofinterest.

In some cases, an optical characteristic may be measured at all pointsin the OCT image. In some cases, the average value and the standarddeviation of the measured optical characteristic for all points in theOCT image may be calculated. A region threshold may be determined basedon the average value and the standard deviation of the measured opticalcharacteristic. For example, the region threshold may comprise twothreshold values to identify data having particular large orparticularly small amplitudes. For example, the threshold values may bethe mean plus three standard deviations and the mean minus threestandard deviations. Alternatively, another multiplier can be used suchas X times so the threshold is the mean plus or minus X times thestandard deviation where X is a variable that can be tuned. In general,a user can adjust the threshold values to adjust the sensitivity of theidentification of a region of interest. For example, the thresholds canbe adjusted to highlight the largest and smallest amplitudes that makeup 20% of the data. Alternatively, the thresholds can be adjusted tohighlight the largest and smallest amplitudes that make up 15% of thedata. Alternatively, the thresholds can be adjusted to highlight thelargest and smallest amplitudes that make up 10% of the data.

The comparison of the measured values for the optical characteristics tothe threshold values can be used to identify a region of interest in theOCT image. Depending on the threshold values, different regions ofinterest will be identified in the OCT image. For example, FIG. 18Ashows an example of B-scan image 600. FIGS. 18B to 18D show differentborders for regions of interest identified in the B-scan image 600 whenthe threshold values are adjusted.

The borders of regions of interest shown in FIGS. 18B to 18D representthe borders of regions based on the optical characteristic ofattenuation in the B-scan image 600. FIG. 18B shows an image 690indicating the borders 695 for the regions of interest in the B-scanimage 600 when the threshold for attenuation is set to identify regionswith relatively high attenuation. FIG. 18C shows an image 700 indicatingthe borders 705 for regions of interest in B-scan image 600 when thethreshold for attenuation is set to identify regions with lower levelsof attenuation than that shown in FIG. 18B. FIG. 18D shows an image 710indicating the borders 715 for regions of interest in B-scan image 600when the threshold for attenuation is set to identify even lower valuesof attenuation as regions of interest as compared to the levels ofattenuation shown in FIG. 18C.

FIGS. 18E to 18G show the OCT image of FIG. 18A with different masksshowing different regions based on various levels of attenuation. Inparticular, the regions of interest shown in FIGS. 18E to 18G haveborders that correspond to the borders of regions of various attenuationlevels as determined in FIGS. 18B to 18D, respectively. As can be seen,adjustment of the region threshold that is used allows more or lessboundaries to be detected and a boundary between layers indicates aregion of interest.

FIG. 18E shows an OCT image 692 with a mask 698 indicating regions ofattenuation in the B-scan image 600 when the threshold for attenuationis set to identify regions with relatively high attenuation as was donein FIG. 18B. FIG. 18F shows an OCT image 702 with a mask 708 indicatingregions of attenuation in the B-scan image 600 when the threshold forattenuation is set to identify regions with mid-levels of attenuation aswas done in FIG. 18C. FIG. 18F shows an OCT image 712 with a mask 718indicating regions of attenuation in the B-scan image 600 when thethreshold for attenuation is set for low levels of attenuation as wasdone in FIG. 18D.

A user may adjust the threshold values for detecting regions of interestto obtain more meaningful information that may indicate a valid boundarybetween two different regions of tissue. The threshold values may, forexample, be adjusted based on the type(s) of tissue that is expected tobe present in the excised tissue sample. The threshold may be increasedin cases where the excised tissue sample is very dense (e.g. a tissuesample with a lower fat content). The threshold may be decreased whenthe tissue sample is less dense (e.g. a tissue sample with higher fattytissue content).

For example, in one embodiment, there are various preset settings thatthe user may choose from to highlight particular regions of interest.These preset settings may correspond to be low, medium or highsensitivity that the user may want to select for various reasons.

For example, a user can select the threshold values based on theexpected density of the tissue sample. Generally, tumor tissue isexpected to be denser than non-tumor tissue; however the density ofnon-tumor tissue can vary amongst patients. Typically, younger andlower-weight patients have higher density tissue, while older andheavier patients have lower density tissue. Thus, in some cases a usermay adjust the threshold values based on at least one of a patient's ageand weight.

At 676, a region of interest is generated based on the comparisonperformed at 674. For a 3D OCT image comprising a plurality of B-scanimages, regions of interest can be generated in each particular B-scanimage. In some cases, there could be many regions of interests or therecould be no regions of interest that are determined. Regions of interestcan be used to identify where there is a region of suspicion in a tissuesample. Further boundary analysis may be used to make an assessment of aboundary to identify a first and a second region. In some cases, regionsof interest may also be generated based on regions of fluorescence in anOCT image.

The regions of interest for a particular B-scan are generated based onthe OCT image data corresponding to that particular B-scan. A consistentregion of interest for the entire 3D OCT image may be generated bycomparing the regions of interest identified in each B-scan image in theplurality of B-scan images, an example of which is illustrated in FIGS.19A to 19D. In particular, FIGS. 19A to 19D show the progression of thegeneration of a consistent border for the regions of interest for aplurality of B-scan images in which the border is relatively consistentacross all B-scan images.

FIG. 19A shows an example set 720 of a plurality of B-scan images 725.FIG. 19B shows a multi-boundary image 730 that shows the borders 735 ofthe regions of interest for each of the plurality of B-scan images 725in the set 720 of B-scan images of FIG. 19A. In this particular example,the borders 735 of the regions of interest shown in FIG. 19B correspondto the borders of regions that have relatively high attenuation and highfluctuation in each B-scan image 725. In other embodiments, variousother additional or alternative optical characteristics may be used todetect regions of interest for the B-scan image 725.

In FIG. 19C, a set 740 of a plurality of flattened B-scan images 745corresponding to the B-scan images 725 is shown. A given flattenedB-scan image 745 can be obtained by using the profile of the tissuesurface for the slice of tissue sample shown in the given B-scan. Aflattened B-scan image generally comprises all the OCT image datacorresponding to the regions in the B-scan that are below the tissuesurface. Accordingly, in a flattened B-scan image, all of the OCT imagedata above the tissue surface may be discarded and the surfaceregistered to the same “height”. This means that if an X,Y,Z coordinatesystem is used, then the surface of the specimen is brought to the same“height” or “Y” value by shifting all of the data in the A-scan so thatall of the surface points are aligned at the same height (this is the“flattening”).

The flattened B-scan images 745 may provide depth information about theborders 735 of the regions of interest across the set 720 of B-scanimages 725. The depth information can be used to identify the marginwidth at a plurality of points in the B-scan images. In other words, theboundary margin can be determined by measuring the distance between anidentified boundary and the tissue surface along the length of theboundary in each B-scan image. The boundary margin width for a givenB-scan image may be determined as the shortest distance between theidentified boundary and the tissue surface along the length of theboundary in the given B-scan image. If there is more than one marginidentified (i.e. more than one B-scan with a margin) then the globalminimum of all of the identified margins may be used as the margin forthe whole specimen.

FIG. 19D shows an image 750 of the borders 755 of the regions ofinterest after undergoing the flattening process. The borders 755 and735 of the regions of interest that are consistent throughout the sets720 and 740 of the B-scan images 725 and 745, respectively, may beidentified. The consistency of the regions of interest can be determinedif the boundaries of the regions of interest are within a certain rangeof pixels of one another. For example, the borders 755 and 735 of theregions of interest in adjacent B-scan images may be considered to beconsistent if the variation of the borders of corresponding regions ofinterest in adjacent B-scan images are within a certain physicalthreshold of one another such as 100 micrometers, for example, whichcorresponds to +/−X pixels where X is an integer that depends on thephysical hardware. Accordingly, the number of pixels that are used inthe threshold depends on the imaging resolution.

The threshold to determine a consistent border of a region of interestacross a plurality of B-scan images may also be selected based on thephysical distance between adjacent B-scan images. For example ifadjacent scans are further apart, the threshold value may be higherbecause more variance between adjacent scans would be expected. In oneembodiment, a threshold value for a consistent border may be determinedas a function of the distance between adjacent scans in the OCT image,such as, but not limited to, a fractional value of the distance betweenadjacent scans.

Any B-scan images that are found not to have a consistent border may bediscarded. Once consistent borders of the regions of interest areidentified, they can be highlighted in each corresponding B-scan image.FIG. 19E shows an example of a B-scan image 600 where the border 610 forthe regions of interest has been considered as being consistent withrespect to the border of adjacent B-scan images. FIG. 19F shows anexample of an image 770 displaying the profile of the consistent border610 of the regions of interest. The other borders that are shown are dueto noise as while the selection of a consistent boundary helps to reducethe number of detected boundaries, some unintended boundaries may stillbe retained.

At 678, the region of interest is displayed on the OCT image. In someembodiments, the borders of the regions of interest can be displayed onthe OCT image. In some embodiments, the regions of interest maythemselves be highlighted in an OCT image. Regions of interest can behighlighted, for example, by applying a mask to the OCT image. Exampleembodiments of OCT images with a mask highlighting regions of interestcan be seen in FIGS. 18E to 18G.

A user may assess one B-scan at a time. Therefore, the differentboundaries that are found to be consistent are retained and used withtheir corresponding B-scan image. The user may also pan through a seriesof B-scan images but the analysis is usually done on a B-scan-by-B-scanbasis.

The boundary identification method 670 is considered to generallyoperate on a window of an OCT image. In the example just given, thewindow of the OCT image may comprise a series of B-scan images (as wasjust described above) or portions of one or more B-scan images. However,in each case the B-scan images (full or partial) comprise a collectionof A-scans. For example, the window of the OCT image may comprise aseries of A-scans from one B-scan image or multiple B-scan images fordifferent scan paths, which may be linear or non-linear. The window canalso be a different group of A-scans in a regular or irregular volume.In some cases, the window of the OCT image may be determined based onfluorescence emitted by a tissue sample. It should be understood thatthe boundary identification method 670 may operate on all of thesedifferent collections of A-scans.

At 680, a boundary in a given OCT image may be identified based on theregion of interest generated at 676. In some embodiments, theidentification of the boundary at 680 may occur automatically. Toidentify a boundary automatically, the boundary identification method670 may measure the optical characteristics of a plurality of points inthe regions of interest of the OCT image. Any suitable method may beused to detect the boundary, such as, but not limited to edge detectionmethods such as the Canny, Sobel and Prewitt edge detection methods.Other methods may also be used which use the measured opticalcharacteristics.

In some alternative embodiments, the boundary identification at 680 maybe based on an input that is received from a user indicating a boundaryin the OCT image after the user views the identified regions of interestin an OCT image. In some embodiments, the user may indicate a boundaryon the OCT image using a suitable input device. For example, if the OCTimage is displayed on a touchscreen display, a user could identify theboundary using a stylus to draw the boundary based on the region ofinterest. A user could also use another suitable input device toindicate a boundary in the OCT image such as, but not limited to, amouse, for example.

In some embodiments, a user may be able to identify a boundary withoutthe generation of a region of interest. For example, a user experiencedin analyzing OCT images may be able to detect a boundary in the OCTimage without a highlighted region of interest but rather, by relying oncertain visual features shown in the OCT image and the user'sexperience. In such a case, the user could indicate the boundary on theOCT image in any suitable manner, as discussed above.

Referring once more to FIG. 16, once a boundary along with correspondingfirst and second regions on either side thereof have been identified inthe OCT image, the tissue assessment method 650 may proceed to analyzethe first region and the second region. For a volume (e.g. a set ofB-scans or a set of A-scans), the boundary can be defined by settingbinary ones (for boundary points) and zeros (for non-boundary points)for each spatial point in the volume.

At 656, a first set of OCT image data is identified that corresponds tothe first region in the OCT image. The OCT image data corresponding tothe first region in the OCT image may comprise the portion of OCT imagedata from each portion of the A-scans that correspond to the firstregion in the OCT image for a 2D OCT image. For a 3D OCT image, theportions of the A-scans that correspond to the first region for each ofthe B-scans that make up the 3D OCT image may be used as the image datacorresponding to the first region.

At 658, a second set of OCT image data is identified that corresponds tothe second region in the OCT image. The OCT image data corresponding tothe second region in the OCT image may comprise the portion of datarepresenting the second region from each portion of the A-scans thatcorrespond to the second region in the OCT image. In some cases, astandard window size can be used to determine the portion of the OCTimage data from each of the first and second regions that will form thefirst and second sets of OCT image data. In some cases, where there islimited tissue above the boundary in the B-scan image, the first andsecond sets of OCT image data will comprise only that portion of the OCTimage data corresponding to a window of the first and second region fromthe boundary with the same width as the boundary margin. The window sizemay be on the order of several hundreds of micrometers and the windowsize may be optimized based on a set of clinical data.

At 660, a first optical dataset is generated for the first set of OCTimage data identified at 656. In some embodiments, the generation of thefirst optical dataset involves measuring at least one opticalcharacteristic in the first set of OCT image data, which involvesperforming calculations on the first set of OCT image data identified at656 to determine the at least one optical characteristic. For example,attenuation may be used as an optical characteristic. However, moreaccurate results may be achieved by using more than one opticalcharacteristic such as, but not limited to, attenuation and fluctuation,for example.

Other examples of the at least one optical characteristic can includeone or more of: area under the profile of an A-scan in the OCT signalintensity data, number of signal maxima in the OCT signal intensitydata, mean distance between signal maxima in the OCT signal intensitydata, standard deviation of the peak distance in the OCT signalintensity data, mean frequency of the OCT signal intensity data,standard deviation of frequencies in the OCT signal intensity data aswell as various other optical properties of the OCT image.

At 662, a second optical dataset is generated based on the second set ofOCT image data identified at 658. The generation of the second opticaldataset can be performed in the same manner as described above withrespect to the first optical dataset. The generation of the secondoptical dataset is performed so that the first optical dataset andsecond optical dataset comprise data representing the same opticalcharacteristics for the first region and second regions respectively.

In an example embodiment, the tissue assessment method 650 can measurethe optical properties of attenuation and fluctuation of the OCT signalintensity for each A-scan in the first and second regions of the OCTimage data for a B-scan image. This may be extended to OCT image datafor a C-scan image by considering each A-scan in the first and secondregions of the OCT image data for all B-scans of the C-scan image. Thismay be extended to OCT image data for other volumes depending on thescan path that is used as previously described.

The attenuation of the OCT signal intensity data may be measured byusing various techniques such as, but not limited to, a linear fit overa window in the A-scan signal intensity data, for example.

In some embodiments (not shown), the tissue assessment method 650 mayalso be used to detect the boundary margin which is the distance fromthe identified boundary to the surface. In some cases, the tissueassessment method may analyze each of the flattened B-scan images shownin FIG. 19C after a consistent boundary has been identified. Theconsistent boundary used for each flattened B-scan image will be theboundary in the flattened B-scan image that was found to be consistentwith the boundaries in the adjacent flattened B-scan images to within anacceptable threshold as previously described. In some cases, theshortest distance between the consistent boundary and the tissue surfaceacross the set of flattened B-scan images may be used as the boundarymargin.

In another embodiment, a tissue assessment method may identify theshortest distance between the consistent boundary and the tissue surfaceacross a set of non-flattened B-scan images to determine the boundarymargin.

In another alternative embodiment, a tissue assessment method canfurther identify the average depth of the boundary margin for a singleB-scan image or a set of B-scan images.

In some alternative embodiments, a tissue assessment method may comparethe second region in a 2D or 3D OCT image to a known OCT image stored ina database which may allow the tissue assessment method 650 to identifythe tissue type in the first, second or subsequent regions of the OCTimage. For example, the database may store ideal cases of OCT image dataknown to correspond to various tissue types such as, but not limited to,adipose tissue, connective tissue, and tumor tissue, for example, andthe given region of the OCT image may be found to be similar to one ofthese tissue types. In this case, the second region is defined herein asbeing the region under and the identified boundary which is beingassessed to determine if it matches a particular type of tissue.

In some alternative embodiments, a tissue assessment method maydetermine that the boundary margin width is less than a marginthreshold. This may indicate that the distance between the boundary andthe tissue surface is too small to obtain meaningful information fromthe OCT image data for the first region. The first region is definedherein as being the region between the surface of the tissue and theidentified boundary. In such a case, the OCT image data corresponding tothe second region can be compared to known OCT image data correspondingto a stored OCT image. The second optical dataset generated for thesecond region would be compared with a known optical dataset based onmeasurements of the same one or more optical characteristics for a knownset of OCT image data corresponding to a known region in the stored OCTimage. This may assist a surgeon in determining if the tissue in thesecond region is cancerous. The second region is defined herein as beingthe region under the identified boundary.

In some alternative embodiments, a tissue assessment method may alsodetect a subsequent boundary in a 2D or 3D OCT image of a tissuespecimen. A subsequent boundary can distinguish between the secondregion and a third region. In some cases, when a subsequent boundary isdetected, the second region can be narrower than the first region. Insuch a case, the window may be determined to be the width of the secondregion at its narrowest point. Identification of a subsequent boundaryis performed in the same manner as described above with regard toidentification of a boundary distinguishing a first region and a secondregion.

Referring now to FIG. 20A, show therein is a flattened window of aB-scan image 600 using a window size that corresponds to the boundarymargin width 640 where boundary 620 was identified. There is a window ofdata in both the first region 622 and the second region 624. If there isnot much tissue above the boundary, then a smaller size may be used forthe window for the first region 622. In some embodiments, the tissueassessment method 650 will analyze the first region 622 and the secondregion 624 for only that portion of the B-scan image where a consistentboundary was identified. A boundary is “accepted” for that portion of aB-scan image for which a consistent boundary was identified acrossadjacent B-scan images in a set of B-scan images. FIG. 20A alsoindicates identifiers for the first optical dataset 872 (the identifieris a circle) and the second optical dataset 874 (the identifier is anX).

FIG. 20B shows an example of an OCT signal intensity plot 790 of thesignal intensity data obtained for an A-scan of the B-scan image 600.The A-scan represents an A-scan profile in the A-scan direction 876 forthe flattened window of the B-scan image 600 shown in FIG. 20A. Theboundary 620 is shown in the OCT signal intensity plot as well as theportions of the plot that correspond to the first region 622 and thesecond region 624.

FIG. 20C shows an example OCT signal intensity plot 800 for the portionof OCT signal intensity plot 790 corresponding to the window of thefirst region 622. A linear fit 805 was determined for the OCT signalintensity data in the OCT signal intensity plot 800. In someembodiments, the linear fit 805 can be determined using a linearregression analysis.

FIG. 20E shows an example OCT signal intensity plot 830 for the portionof the OCT signal intensity plot 790 corresponding to the window of thesecond region 624. A linear fit 835 was determined for the OCT signalintensity data in OCT signal intensity plot 830.

As can be seen from a comparison of FIG. 20C and FIG. 20E, the slope ofthe linear fit of the OCT signal intensity data may differ depending onthe tissue region. For example, significant differences in signalattenuation between two regions may indicate different tissue types.

In some cases, the attenuation of the OCT signal intensity data can bedetermined from the low frequency component of the OCT signal intensitydata alone. The low frequency component of the OCT signal intensity datacan be isolated using any suitable signal processing technique such as,but not limited to, a low pass filter, a Gaussian filter and the like.

A Gaussian filter can also be used to normalize the OCT signal intensityplot 800. FIG. 20D shows a normalized OCT signal plot 820 correspondingto a normalization of OCT signal intensity plot 800 using a Gaussianfilter. The low frequency data component identified using the Gaussianfilter can be subtracted from the data shown in the OCT signal intensityplot 800 to generate a normalized OCT signal plot 820.

Similarly FIG. 20F shows a normalized OCT signal plot 860 correspondingto a normalization of the OCT signal intensity plot 830 for the secondregion using a Gaussian filter.

In some embodiments, the standard deviation of the normalized OCT signalplot is measured over a window from the boundary for each region. InFIG. 20D, the standard deviation 822 has been calculated for normalizedOCT signal plot 820. Similarly, FIG. 20F shows the standard deviation862 calculated for normalized OCT signal plot 860. The standarddeviation of the normalized OCT signal plot is a measurement of signalvariation (i.e. fluctuation of OCT signal intensity) through a region ofinterest for the tissue sample that provides an indication of thetexture of the region of interest for the OCT image.

In some embodiments, both the attenuation and the texture of the OCTsignal intensity data can be used as measured values for two opticalcharacteristics o1 and o2 for the portion of each A-scan in the windowin the first region and for the portion of each A-scan in the window inthe second region and can be represented in the format (o11, o21) forthe portion of the A-scan in the first region and (o12, o22) for theportion of the A-scan in the second region. In this example, the firstindex represents the optical measurement being made and the second indexrepresents the region in which the measurement is made. For example, o11is a measure of the first optical characteristic in the first region. Adataset can be generated based on these values for the first region andthe second region by measuring these values for each portion of eachA-scan in the first and second regions.

FIG. 20G shows an example of a scatter plot of measured optical data inwhich one measured optical characteristic (e.g. texture) is plottedalong the y axis and a second measured optical characteristic (e.g.attenuation) is plotted along the x axis. The data from each portion ofthe A-scans provides a data point with x and y coordinates (e.g. (o11,o21) or (o21, o22)) that can be plotted on the scatter plot.Accordingly, the dataset plot 870 shows a plot of attenuation versustexture for the first optical dataset 872 and the second optical dataset874. In some embodiments, different and further optical characteristicsmay also be used. Thus, a dataset can be generated for each regionhaving a dimension depending on the number of measured opticalcharacteristics and the data points can be plotted on a correspondingmulti-dimensional scatter plot.

At 664, the first optical dataset and the second optical dataset arecompared to identify a degree of difference between the first region andthe second region. In some embodiments, the tissue assessment method 650may calculate a discrimination score based on the comparison of thefirst optical dataset with the second optical dataset. A degree ofdifference between the first region and the second region can beidentified based on the discrimination score. In some embodiments, thetissue assessment method 650 can use an optimization method or suitablemachine learning method to calculate the discrimination score such as,but not limited to, a support vector machine method, a k-nearestneighbors method, a decision tree learning method, a random forestmethod, a native Bayes method and a quadratic decision boundary method,for example. In embodiments using a support vector machine, any suitabletype of support vector machine can be used such as, but not limited to,a linear support vector machine and a radial bias function supportvector machine.

Furthermore, it should be noted that the support vector machine method,as well as the other methods, is scalable to more or less dimensionswhere each dimension corresponds to a measurement of a different opticalcharacteristic. By adding another optical characteristic so that thereare three optical characteristics, the differentiation can be performedin more dimensions (i.e. three axes rather than two axes), or it can beperformed in 1D (on a line, to find the optimal separation if only oneoptical measurement is used. However, it should be noted that with thedifferent number of optical characteristics, there is a change in thethreshold for what is defined as being “actionable”. For example, themore optical characteristics (which can be considered to be variablesfor the optimization methods) that are used to make a differentiation,the easier it is to separate the variables. For example, if only oneoptical characteristic is used it may be difficult to get over 60%differentiation (in 1D separation). But if 3 or 4 different opticalcharacteristics are used (i.e. in 3D or 4D), it is typically easier toget over 90 or 95% differentiation for the same area, provided thatthese areas are in fact different from one another. The thresholds usedfor differentiation are determined depending on the number of opticalcharacteristics that are used.

As can be seen from FIG. 20G, the datasets corresponding to the tworegions in the OCT image appear to have different opticalcharacteristics. Regions with different optical characteristics may beindicative of an actionable boundary. Act 664 of the tissue assessmentmethod 650 provides a discrimination score representing the maximumdifferentiability between the first and second optical datasets. Sincethe discrimination score represents the degree of difference between tworegions, the discrimination score may be used to quantify the amountthat the two regions differ from one another.

FIG. 21A shows an example scatter plot 880 where a linear support vectormachine is used to determine a discrimination score 882 for the firstoptical dataset 872 and the second optical dataset 874. In the exampleshown in FIG. 21A, the two regions were determined to have adiscrimination score of 0.92. In this case, the closer the value of thediscrimination score is to 1, the higher the degree of differentiationbetween the first and second optical datasets meaning that the first andregions are more likely to be different from one another.

FIG. 21B shows an example scatter plot 880 where a support vectormachine with a radial bias function is used to calculate adiscrimination score 892 for the first optical dataset 872 and thesecond optical dataset 874. In the example shown in FIG. 21B, the tworegions were determined to have a differentiation score of 0.94indicating that there is a high degree of differentiation between thefirst and second optical datasets.

The degree of difference, provided by the discrimination score, may beused to identify when a boundary may be considered to be actionable by asurgeon. A high degree of difference may suggest that the two regionsare different tissue types. Based on the degree of difference determinedby the tissue assessment method 650, a surgeon may be able to decidewhether further tissue must be excised intraoperatively. As described,in at least some embodiments, the tissue assessment method may alsoindicate the width of the margin for all points in the OCT image, whichmay further assist a surgeon in determining whether the boundary isactionable (i.e. if the width of the margin is less than a desiredamount when the boundary is determined to be actionable).

FIGS. 22A to 22F show an example of output results from the tissueassessment method 650 for a tissue sample in which a non-actionableboundary is detected. The OCT images shown in FIGS. 22A to 22Fcorrespond to the various acts of the tissue assessment method 650.

FIG. 22A shows a B-scan image 900 after a boundary 906 has beenidentified. The boundary 906 distinguishes a first region 902 from asecond region 904.

FIG. 226 shows two windows of flattened B-scan image 920 for the regionover which boundary 906 was accepted. The flattened B-scan image 920 isgenerated based on the identification of the surface of the tissue shownin the B-scan image 900. As can be seen from FIG. 22B, the window 926over which each of the first and second regions is analyzed is the same.

FIG. 22C shows a scatter plot 930 of the measured opticalcharacteristics for the two regions identified in FIG. 22B. The scatterplot 930 shows the optical datasets generated for the first region 922and the second region 924 respectively. It seems that the measuredoptical characteristics of the two regions shown in FIG. 22C appear tobe more similar to one another than the measured optical characteristicsof the two regions plotted in FIG. 20G.

FIGS. 22D to 22E show the results of different optimization methodsapplied to the first and second datasets for the regions of interestshown in FIG. 22C. In FIG. 22D, a linear support vector machine methodwas used and in FIG. 22E a radial bias function support vector machinemethod was used. The linear support vector machine method output 940 ofFIG. 22D determined a discrimination score 942 for the two regions of0.75. The radial bias function support vector machine method output 950shown in FIG. 22E determined a discrimination score 952 for the tworegions of 0.76.

It is apparent that the outputs of the optimization methods used for thefirst and second regions in FIG. 22 indicate that these regions are moresimilar to one another than the two regions of tissue analyzed in FIG.21. Since the optimization method indicated a lower degree ofdifferentiability between the two regions, this suggests that anactionable boundary is not present in the tissue sample shown in aB-scan image 900.

FIG. 22F shows the B-scan image 900 with a mask 960 overlaid indicatingthe results of the tissue assessment method 650. The continuous hatchingin mask 960 the first region and the second region indicates that thereis not a high degree of difference between the first and second regionson either side of the boundary 906.

The tissue assessment method 650 may be modified to include otheroptical characteristics such as, area under the profile of an A-scan inthe OCT signal intensity data, the number of signal maxima in the OCTsignal intensity data, the mean distance between signal maxima in theOCT signal intensity data, the standard deviation of the peak distancein the OCT signal intensity data, the mean frequency of the OCT signalintensity data, the standard deviation of the frequencies in the OCTsignal intensity data and various other optical properties of the OCTimage. Different optical characteristics may be used in the tissueassessment method 650 since breast lumpectomy specimens have uniqueoptical properties compared to other tissue samples such as blood vesselstructures or retinal structures.

In some embodiments, the tissue assessment method 650 can be modified toidentify a boundary in each B-scan image of the OCT image. For example,in these alternative embodiments, for each B-scan image, the modifiedtissue assessment method can identify a first and second set of OCTimage data corresponding to the first and second regions of interestrespectively in a B-scan image. The modified tissue assessment methodcan then generate first and second B-scan optical datasets for eachB-scan image in the same manner as was described above. The tissueassessment method can then compare the first and second B-scan opticaldatasets for each B-scan image to determine the degree of differencebetween two regions in each B-scan.

In some embodiments, the tissue assessment method 650 may further bemodified to determine the discrimination score for each B-scan image ina plurality of B-scan images. In some embodiments, the modified tissueassessment method can determine the degree of difference between the tworegions in the 3D OCT image based on the average discrimination scorefor the plurality B-scan images that make up the 3D OCT image. In someembodiments, the data in the first and second B-scan optical datasetsfor all B-scan images may be compiled into first and second opticaldatasets for the OCT image. The first and second optical datasets canthen be compared to determine the degree of difference between the firstand second region for the OCT image.

In some embodiments, one of the first and second regions that are usedin the tissue assessment method 650 may be from an identified image froma data store that has been previously been identified as being aparticular type of tissue. The method would operate in a similar fashionsince a boundary would still need to be detected but opticalcharacteristics for the identified image would likely already have beenmeasured and stored in the data store and the measured data can beaccessed instead of re-determined for the identified image.

While OCT is an established medical imaging technique, it is alsoapplicable and being increasingly used in industrial applications. Someof these industrial applications may include non-destructive testing(finding manufacturing defects, for example), welding quality assurance,material thickness measurements (for silicon wafers, for example), forindustrial painting (e.g. verifying uniform thickness) and surfaceroughness characterization.

It should be noted that in alternative embodiments, the wide field OCTimaging methods described herein may be used for industrialapplications, such as one or more of the aforementioned industrialapplications, in terms of obtaining images of a greater portion of anobject's surface and underlying layers that would otherwise not bepossible based on the field of view of a conventional OCT imagingsystem.

It should also be noted that in alternative embodiments, the tissueassessment method may be used for industrial applications, such as oneor more of the aforementioned industrial applications, in terms ofdetermining whether two regions near an object's surface are differentfrom one another or different in comparison to a region of a knownmaterial for the object. In these cases, the tissue assessment methodcan be referred to as an assessment method or a region assessmentmethod.

The various embodiments of the tissue assessment method described hereinmay be implemented by the imaging system 10 including the associatedhardware and software components shown in FIG. 5. However, moregenerally, the various embodiments of the tissue assessment methodsdescribed herein may be implemented using a combination of hardware andsoftware. These embodiments may be implemented in part using computerprograms executing on a programmable device that includes at least oneprocessor, an operating system, one or more data stores (includingvolatile memory or non-volatile memory or other data storage elements ora combination thereof), at least one communication interface and/or userinterface as well as any other associated hardware and software that isnecessary to implement the functionality of the various embodimentsdescribed herein. For example, and without limitation, the computingdevice may be a server, a network appliance, an embedded device, acomputer expansion module, a personal computer, a laptop, a personaldata assistant, a smart-phone device, a tablet computer, a wirelessdevice or any other computing device capable of being configured tocarry out the tissue assessment methods described herein.

In some embodiments, the communication interface may be a networkcommunication interface, a USB connection or another suitable connectionas is known by those skilled in the art. In other embodiments, thecommunication interface may be a software communication interface, suchas those for inter-process communication (IPC). In still otherembodiments, there may be a combination of communication interfacesimplemented as hardware, software, and a combination thereof.

In at least some of the embodiments described herein, program code maybe applied to input data to perform at least some of the functionsdescribed herein and to generate output information. The outputinformation may be applied to one or more output devices, for display orfor further processing.

At least some of the embodiments described herein that use programs maybe implemented in a high level procedural or object oriented programmingand/or scripting language or both. Accordingly, the program code may bewritten in C, Java, SQL or any other suitable programming language andmay comprise modules or classes, as is known to those skilled in objectoriented programming. However, other programs may be implemented inassembly, machine language or firmware as needed. In either case, thelanguage may be a compiled or interpreted language.

The computer programs may be stored on a storage media (e.g. a computerreadable medium such as, but not limited to, ROM, magnetic disk, opticaldisc) or a device that is readable by a general or special purposecomputing device. The program code, when read by the computing device,configures the computing device to operate in a new, specific andpredefined manner in order to perform at least one of the methodsdescribed herein.

Furthermore, some of the programs associated with the various methodsdescribed herein are capable of being distributed in a computer programproduct comprising a computer readable medium that bears computer usableinstructions for one or more processors. The medium may be provided invarious forms, including non-transitory forms such as, but not limitedto, one or more diskettes, compact disks, tapes, chips, and magnetic andelectronic storage. In alternative embodiments the medium may betransitory in nature such as, but not limited to, wire-linetransmissions, satellite transmissions, internet transmissions (e.g.downloads), media, digital and analog signals, and the like. Thecomputer useable instructions may also be in various formats, includingcompiled and non-compiled code.

FIG. 23A shows a perspective view of an example embodiment of a samplecontainer 1000 for containing a sample, such as a tissue sample orspecimen. The container 1000 can be used for a variety of purposesincluding loading the sample into the imaging system 10, securing thesample during various types of imaging and scanning such as, but notlimited to OCT scanning, for example, and transporting the samplethrough the clinical process.

The container 1000 may be used to provide a systematic means ofcommunicating tissue sample orientation information relative to areference point, such as on a patient's body for example, by usingcertain markers such as, but not limited to, radio opaque tags andimaging marking beads, for example. The container 1000 may also be usedto prevent sample mix-up and enforce a single-usage policy through theuse of mechanical tabs and/or RFID tags, for example. The container1000, or a variant thereof, may also include a peel-off mechanism toexpose a sterile portion of the container 1000 that interfaces with atissue handling system of the imaging system 10. For example, a portionof the container 1000 may be initially covered by a material so thatthere is no direct contact between the operator and that portion of thecontainer 1000 during the loading and assembly of the container 1000.The protected portion may then be exposed by peeling the protectivematerial off of the container 1000 prior to loading the container intothe imaging system 10. This may prevent blood or other fluids on theoperator's gloves from being transferred into the scanning region of theimaging system 10. Furthermore, the container 1000, or a variantembodiment thereof, may provide a means of trimming a guide wire priorto scanning. This may be realized by a detachable wire cutter mechanismresembling a nail trimmer that is packaged as an integral part of thecontainer 1000.

Some current methods for maintaining orientation information of tissuesamples that are extracted from a human body, or other object, involvethe application of coloring agents to the tissue sample to markorientation, or the application of sutures to the tissue sample whichact as fiducials. However, such methods rely upon a systematicapplication of these orientation cues by the surgeons. A failure tofollow the orientation marking protocol results in the loss of theorientation information when the tissue specimen reaches variousclinical members for detailed analysis of the tissue specimen. Amongstother reasons, this information is vital because the pathologist has tocommunicate the location of surfaces with suspicious margins back to thesurgeon so that the surgeon can extract an additional amount of tissuefrom these specific regions if needed.

The container 1000, and variants thereof, may provide safe andconsistent handling of a tissue sample when attempting to image itsentire surface. For example, the container 1000 permits safe inversionof a contained tissue sample so that opposing surfaces can be imaged (orscanned). As another example, the container 1000 may include orientationcues (or fiducials) that may permit consistent handling of the containedtissue sample by preserving the orientation of the contained tissuesample. In some embodiments, the contained tissue sample may be uniquelyassociated with particular patient information through the use ofbar-codes and/or RFID tags applied to the container 1000. This mayprovide a unique association between the patient information and thetissue sample that may reduce the mix-up of patient samples. In someexample embodiments, the RFID tag or mechanical tabs can be further usedto enforce single use of the container 1000 so that the potential forcross-contamination of tissue samples is minimized. Finally, thecontainer 1000 may include an integrated trimmer tool that may be usedto cut the guide wire, which is typically placed in a suspect region ofthe tissue sample as a pre-operative procedure, prior to imaging thetissue sample.

The container 1000 is shown including a top sample support 1002, aninterface sleeve 1004 and a bottom sample support 1006. FIG. 23B shows aperspective view of the bottom sample support 1006 and a tissue sample1008. As shown, the bottom sample support 1006 may be used as a platformto support the tissue sample 1008. FIG. 23C shows a cross-sectional viewtaken along line 23C-23C in FIG. 23B. In the example shown, the bottomsample support 1006 includes a base 1014, and side walls 1016 thatdepend therefrom.

As shown, an optional foam layer 1012 overlies the base 1014 between theside walls 1016 of the bottom sample support 1006 to define an uppersurface 1010 for supporting the tissue sample 1008. In some cases, thefoam layer 1012 may reduce the deformation of the tissue sample 1008that is supported by the bottom sample support 1006. For example, thefoam layer 1012 may compress beneath the tissue sample 1008 to at leastpartially conform to the shape of the tissue sample 1008. Still, inalternative embodiments, the bottom sample support 1006 does not includethe foam layer 1012. In one example embodiment, the tissue sample 1008may be directly supported by an upper surface 1011 of the base 1014.

In some examples, the base 1014 may be made from a rigid material (e.g.rigid plastic or metal). Alternatively, the base 1014 may be made from acompliant material (e.g. fabric, film, mesh, or foam). For example, thebase 1014 may be suspended in tension between the side wall(s) 1016. Insome cases, the base 1014 may be made of compliant material to reducethe deformation of the tissue sample 1008 that is supported by thebottom sample support 1006. For example, when the base 1014 is made ofcompliant material the base 1014 may stretch, compress, or otherwisedeform to at least partially conform to the shape of the tissue sample1008 supported thereon.

The bottom sample support 1006 as exemplified includes orientationmarkers 1018 for orienting the tissue sample 1008. In use, the surgicalteam may place an excised tissue sample 1008 onto the bottom samplesupport 1006 such that the orientation markers 1018 accuratelycorrespond with the orientation of the tissue sample 1008 prior toexcision in the patient's body. This may improve clinical workflow bymaintaining and communicating the orientation of the tissue sample 1008from the Operating Room (OR) to the pathology department. In turn, thismay permit a margin analysis of the excised tissue sample 1008 by thepathology department to more accurately identify the location ofadditional tissue to be excised from the patient's body in the OR.Still, in alternative embodiments, the bottom sample support 1006 maynot include the orientation markers 1018 but includes other orientationmarkers.

FIG. 23D shows a perspective view of the top sample support 1002. Thetop sample support 1002 may be used to support a tissue sample 1008.FIG. 23E shows a cross-sectional view taken along line 23E-23E in FIG.23D, with the top sample support 1002 positioned above the tissue sample1008 and additionally including an optional foam layer 1022. In theexample shown, the top sample support 1002 includes a base 1020, andside wall(s) 1025 that depend therefrom.

FIG. 23E shows an embodiment of the top sample support 1002 including anoptional foam layer 1022 that overlies the base 1020 between the sidewalls 1025 to define a lower surface 1024 for supporting the tissuesample 1008 when the container 1000 is inverted. In some cases, the foamlayer 1022 may reduce the deformation of a tissue sample 1008 that issupported by the top sample support 1002. For example, the foam layer1022 may compress to at least partially conform to the shape of thetissue sample 1008.

FIG. 23D shows an alternative embodiment of a top sample support 1002that does not include the foam layer 1022. In the example shown, thebase 1020 includes a lower surface 1024 that makes direct contact with asupported tissue sample 1008. In some examples, the base 1020 may bemade from a rigid material (e.g. rigid plastic or metal). Alternatively,the base 1020 may be made from a compliant material (e.g. fabric, film,mesh, or foam). For example, the base 1020 may be suspended in tensionbetween the side wall(s) 1025. In some cases, when the base 1020 is madeof compliant material it may reduce the deformation of a tissue sample1008 that is supported by the top sample support 1002. For example, whenthe base 1020 is made of compliant material it may stretch, compress, orotherwise deform to at least partially conform to the shape of thetissue sample 1008 supported there against.

FIGS. 23F and 23G show front and rear perspective views of an interfacesleeve 1004, in accordance with at least one embodiment. In the exampleshown, the interface sleeve 1004 includes side wall(s) 1026 that extendbetween an open lower end 1028 and an open upper end 1030 of theinterface sleeve 1004. As shown, the side wall(s) 1026 define aninterior volume 1032 for receiving a tissue sample 1008. Optionally, alabel 1034 or other identifying object, such as an RFID tag (not shown),for example, can be placed on an interface sleeve 1004 (e.g. on anexterior surface of the side wall(s) 1026). This may permit thecontainer 1000 to provide information such as patient information,physician information, tissue sample information, and container usageinformation.

In some examples, an interface sleeve 1004 is substantially transparentto one or more imaging techniques. Some examples of imaging techniquesinclude, without limitation, photography, radiography, magneticresonance imaging (MRI), ultrasound, computed tomography (CT), andx-ray. This may permit the tissue sample 1008 to be imaged through theinterface sleeve 1004 while being contained within the container 1000.In some examples, the interface sleeve 1004 may be transparent to lightin the visible spectrum, which may permit imaging of a contained tissuesample 1008 by conventional photography and visual inspection by amedical practitioner. Alternatively or additionally, the interfacesleeve 1004 may be radiographically transparent, which may permitimaging of a contained tissue sample 1008 by radiation-based imagingtechniques (e.g. x-ray imaging). Still, in alternative embodiments, theinterface sleeve 1004 may be substantially opaque to one or more imagingtechniques. In these cases, imaging of the tissue sample 1008 containedwithin the container 1000 by certain imaging techniques may require theinterface sleeve 1004 to be removed.

The interface sleeve 1004 as exemplified includes orientation markers1036 distributed about the circumference of the interface sleeve 1004.The orientation markers 1036 may be included in addition to or as analternative to the orientation markers 1018 on the bottom sample support1006. In some examples, the orientation markers 1036 are at leastpartially opaque to one or more imaging techniques. This may permit anorientation marker 1036 to be automatically rendered in images producedby these one or more imaging techniques, thereby labeling theorientation on the rendered images. Still, in alternative embodiments,the interface sleeve 1004 does not include the orientation markers 1036.

Referring now to FIGS. 24A to 24F, shown therein are illustrations ofhow a tissue sample 1008 is placed in the container 1000 and thecontainer 1000 is closed. FIG. 24A shows a tissue sample 1008 placed onthe bottom sample support 1006. In some cases, the orientation of thetissue sample 1008 can be aligned with the orientation markers 1018 onthe side walls 1016 of the bottom sample support 1006.

FIG. 24B shows the top sample support 1002 and the bottom sample support1006 inserted into the interface sleeve 1004. FIG. 24C shows across-sectional view taken along line 24C-24C in FIG. 24B. As shown, theside walls 1016 and 1025 of the bottom sample support 1006 and the topsample support 1002 respectively, are sized to be received inside theinterior volume 1032 between the side walls 1026 of the interface sleeve1004. The side walls 1016, 1025, and 1026 as shown are cylindrical, andthe outside diameter of side walls 1016 and 1025 is less than the insidediameter of side walls 1026. This may permit at least the portions ofthe top sample support 1002 and the bottom sample support 1006 definedby side walls 1016 and 1025 to slide inside of the interface sleeve1004. In alternative embodiments, the side walls 1016, 1025, and 1026have a different shape, such as cuboid for example.

In the example shown, the bottom sample support 1006 is releasablyengageable with the interface sleeve 1004 for selectively closing thelower end 1028 of the interface sleeve 1004. In some embodiments, thebottom sample support 1006 and the interface sleeve 1004 may include oneor more mating retentive members for releasably engaging the bottomsample support 1006 to the interface sleeve 1004. As shown, the bottomsample support 1006 includes a plurality of locking pins 1038 thatextend radially outwardly from the side walls 1016. Further, the innersurface of the side walls 1026 of the interface sleeve 1004 is shownincluding a plurality of lower guide channels 1040 that extend from thelower end 1028 toward the upper end 1030. Each lower guide channel 1040is sized to receive a locking pin 1038. A lower locking slot 1042 isshown extending tangentially from each lower guide channel 1040.

In use, the bottom sample support 1006 can be positioned below the lowerend 1028 of the interface sleeve 1004 and rotated to align each lockingpin 1038 with a lower guide channel 1040. The bottom sample support 1006may then slide into the interface sleeve 1004 with each locking pin 1038traveling inside a corresponding lower guide channel 1040. When eachlocking pin 1038 is positioned at an entry to a lower locking slot 1042,the bottom sample support 1006 can be rotated to move each locking pin1038 into the corresponding lower locking slot 1042 thereby releasablyengaging the bottom sample support 1006 with the interface sleeve 1004and closing the lower end 1028. Together, each locking pin 1038 andlower locking slot 1042 may form a bayonette-style locking mechanismwhereby each lower locking slot 1042 provides resistance to entry andexit of a locking pin 1038. This resistance can be overcome by anapplication of sufficient force.

Optionally, the bottom sample support 1006 includes a flange 1044 sizedto prevent further insertion of the bottom sample support 1006 inside ofthe interface sleeve 1004. As shown, the flange 1044 extends radiallyoutwardly from the side walls 1016 and has a diameter that is largerthan the inside diameter of the side walls 1026 of the interface sleeve1004. This may prevent the bottom sample support 1006 from becomingentirely received inside the interior volume 1032 of the interfacesleeve 1004. In some cases, the flange 1044 provides a gripping surfacefor the hands of a user or the elements of a machine to grasp the bottomsample support 1006 (e.g. when engaging or disengaging the bottom samplesupport 1006 and the interface sleeve 1004). Still, in alternativeembodiments, the bottom sample support 1006 may not include a flange1044.

In the example shown, the top sample support 1002 is releasablyengageable with the interface sleeve 1004 at a plurality of longitudinalpositions between the upper end 1030 and the lower end 1028. In someembodiments, the top sample support 1002 and the interface sleeve 1004include one or more mating retentive members for releasably engaging thetop sample support 1002 to the interface sleeve 1004. As shown, the topsample support 1002 includes a plurality of locking pins 1046 thatextend radially outwardly from side walls 1025. Further, the innersurface of the interface sleeve 1004 is shown including a plurality ofupper guide channels 1048 that extend from the upper end 1030 toward thelower end 1028. A plurality of longitudinally distributed spaced-apartupper locking slots 1050 are shown extending tangentially from eachupper guide channel 1048.

In the example shown, the locking pins 1046, upper guide channels 1048and upper locking slots 1050 are configured analogously to the lockingpins 1038, lower guide channels 1040, and lower locking slots 1042. Inuse, the top sample support 1002 is positioned above the upper end 1030of the interface sleeve 1004, as shown in FIG. 24A, and rotated to aligneach locking pin 1046 with an upper guide channel 1048. The top samplesupport 1002 can then slide into the interface sleeve 1004 with eachlocking pin 1046 traveling inside a corresponding upper guide channel1048, as shown in FIG. 24C. Upper locking slots 1050 provide a pluralityof resting positions at which a locking pin 1046 can be releasablyengaged. The top sample support 1002 can be moved longitudinally intothe interface sleeve 1004 until the tissue sample 1008 is held firmlybetween the top sample support 1002 and the bottom sample support 1006,as shown in FIG. 24D. The top sample support 1002 can then be rotated tomove each locking pin 1046 into a corresponding upper locking slot 1050thereby releasably engaging the top sample support 1002 with theinterface sleeve 1004 and closing the upper end 1030.

As shown, the top sample support 1002 is pressed downwards so that thetop sample support 1002 and the bottom sample support 1006 firmly holdthe tissue sample 1008 in place. Once, the locking pins 1038 and 1046are moved into locking slots 1042 and 1050, locking slots 1042 and 1050lock the position of the top and bottom sample supports 1002 and 1006relative to the interface sleeve 1004 against both radial and axialforces. This may prevent the tissue sample 1008 from shifting within thecontainer during handling when the top and bottom sample supports 1002and 1006 are disposed to firmly engage the tissue sample 1008. In turn,this may preserve the relationship between the tissue sample 1008 andthe orientation markers 1018 and/or 1036.

FIG. 24E shows a portion of the interface sleeve 1004 at the upper end1030. As shown, an inside of side wall 1026 is provided with upper guidechannels 1048 that extend longitudinally from the upper end 1030. In theexample shown, upper guide channel 1048 has a radiused inlet 1054 at theupper end 1030. This may provide for easy insertion of a locking pin1046 of the top sample support 1002. Similarly, lower guide channels1040 provided at the lower end 1028 may have a similarly radiused inlet.In alternative embodiments, one or more (even all) of the guide channels1040 and 1048 does not include a radiused inlet. For example, theseguide channels 1040 and/or 1048 may include inlets having square-cutcorners.

In some embodiments, one or both of the top and bottom sample supports1002 and 1006 may include gasket(s) (e.g. o-rings, not shown) to achievea liquid seal with the interface sleeve 1004. Alternatively or inaddition, the interface sleeve 1004 may include gasket(s) (not shown) tosupport a liquid seal with one or both of the top and bottom samplesupports 1002 and 1006. In other embodiments, none of the top and bottomsample supports 1002 and 1006, and the interface sleeve 1004 may includea gasket.

Referring again to FIGS. 24A to 24D, the top sample support 1002optionally includes a flange 1051 sized to prevent further insertion ofthe top sample support 1002 inside of the interface sleeve 1004. Asshown, the flange 1051 extends radially outwardly from the side walls1025 and has a diameter that is larger than the inside diameter of theside walls 1026 of the interface sleeve 1004. This may prevent the topsample support 1002 from becoming entirely received inside the interiorvolume 1032 of the interface sleeve 1004. In some cases, the flange 1051provides a gripping surface for the hands of a user or the elements of amachine to grasp the top sample support 1002 (e.g. when engaging ordisengaging the top sample support 1002 and the interface sleeve 1004).Still, in alternative embodiments, the top sample support 1002 may notinclude a flange 1051.

In the example shown, each of the top sample support 1002 and the bottomsample support 1006 can be disengaged from the interface sleeve 1004 bytwisting each relative to the interface sleeve 1004 to remove thelocking pins 1038 and 1046 from the locking slots 1042 and 1050 and thenmoving each longitudinally away from the interface sleeve 1004 towithdraw each from the interface sleeve 1004.

As shown in FIG. 24D, the base 1014, base 1020, and the optional foamlayers 1012 and 1022 are concave with respect to the tissue sample 1008.This may permit the top and bottom sample supports 1002 and 1006 to keeppressure on the tissue sample 1008 directed inwards towards the centralaxis 1053 of the container 1000. Still, in alternative embodiments, oneor more (even all) of the base 1014, base 1020, and the optional foamlayers 1012 and 1022 may not be concave with respect to the tissuesample 1008. For example, one or more of the base 1014, base 1020, andthe optional foam layers 1012 and 1022 may be flat or convex withrespect to the tissue sample 1008.

As exemplified in FIG. 24A, each of the top sample support 1002,interface sleeve 1004, and the bottom sample support 1006 may includemachine interface features 1052. The machine interface features maypermit the scanning assembly 80 of the imaging system 10 tosystematically grip the container so that portions of it can be removedand re-attached as part of the scanning process. In the example shown,each machine interface feature 1052 comprises a bore for receiving acorresponding post of the tissue handling system. Alternatively, or inaddition, one or more of the machine interface features 1052 may have adifferent structure that accommodates gripping by the tissue handlingsystem, such as outwardly extending posts for example. Still, inalternative embodiments, one or more (even all) of the top samplesupport 1002, interface sleeve 1004, and the bottom sample support 1006may not include the machine interface features 1052.

As shown most clearly in FIGS. 23D and 24A, the bases 1014 and 1020 areat least permeable to air. In the example shown, each of the bases 1014and 1020 include a plurality of vent holes 1056. This may permit ambientair to permeate the container 1000 and thereby regulate the humidityinside the container 1000. In turn, this may help to preventcondensation from forming inside of the container 1000. Condensation canappear translucent or opaque to some imaging techniques and thusinterfere with capturing clear images. In some cases, the vent holes1056 may also permit gas or fluid from the tissue sample 1008 to escapethe container 1000 (e.g. during scanning). In some embodiments, one ormore of the foam layers 1012 and 1022 is also permeable to air. Inalternative embodiments, one or more (even all) of the bases 1014 and1020, and the foam layers 1012 and 1022 may be impermeable to air.

It will be appreciated that locking pins and locking slots are one ofmany examples of mating retentive members that permit the top and bottomsample supports 1002 and 1006 to releasably engage the interface sleeve1004. Reference is now made to FIGS. 25A and 25B, which shows acontainer 1100 in accordance with another embodiment. FIG. 25A shows anexploded perspective view of the container 1100. FIG. 25B shows across-sectional view of the container 1100 taken along line 25B-25B inFIG. 25A.

In the example shown, the container 1100 includes a top sample support1102, an interface sleeve 1104, a bottom sample support 1106, and abottom lid 1108. As shown, the retentive members of the container 1100include latches 1110 that can releasably engage notches (or teeth) 1112.

The top and bottom sample supports 1102 and 1106 as shown as beingstructurally identical. This may permit the top and bottom samplesupport 1102 and 1106 to be interchangeable. In alternative embodiments,the top and bottom sample supports 1102 and 1106 may not be structurallyidentical and thus not interchangeable. As shown, each sample support1102 and 1106 includes a base 1113 and side walls 1114 that dependtherefrom. A pair of latches 1110 are shown extending radially outwardlyof the side walls 1114 through openings 1116 in the side walls 1114. Asexemplified, each pair of latches 1110 may be biased in position by abiasing member 1118.

In the example shown, each biasing member 1118 includes a grippingmember 1120 and two resilient arms 1122. A latch 1110 is shown extendingradially outwardly from each gripping member 1120. As shown, eachgripping member 1120 is held in sliding contact with the base 1113 by apair of catches 1123 that limits the gripping member 1120 to movement ina radial direction. As exemplified, radially inward movement of eachgripping member 1120 is effected by resilient bending of thecorresponding pair of resilient arms 1122. Each latch 1110 can bewithdrawn radially inwardly of the side walls 1114 by applyingsufficient radial inward force to the gripping member 1120 to bend theresilient arms 1122. While bent, the resilient arms 1122 exert a biasingforce urging the gripping member 1120 and the latch 1110 radiallyoutwardly to return to the resting position shown in FIGS. 25A and 25B.

The interface sleeve 1104 as shown includes side walls 1124 which extendfrom an open lower end 1126 to an open upper end 1128 to define aninterior volume 1130. As shown, a plurality of longitudinally spacedapart arranged notches (or teeth) 1112 are provided on an interior faceof the side walls 1124.

In some embodiments, the sample supports 1102 and 1106, and theinterface sleeve 1104 include mating alignment ribs and alignmentchannels. As shown, the sample supports 1102 and 1106 include alignmentribs 1132 that extend radially outwardly of the side walls 1114. Eachalignment rib 1132 is sized and positioned to be received by acorresponding alignment channel 1134 provided on an interior of the sidewall 1124 of the interface sleeve 1104. Further, the interior of theside wall 1124 of the interface sleeve 1104 includes a radially inwardlyextending alignment rib 1136 that also extends longitudinally along theinterior of the side wall 1124 and is sized and positioned to bereceived by a corresponding alignment channel 1138 provided on anoutside of the side walls 1114.

In some embodiments, the alignment ribs and alignment channels of thecontainer 1100 are configured to enforce an orientation relationshipbetween each sample support 1102 and 1106, and the interface sleeve1104. For example, each alignment rib and/or alignment channel of eachsample support 1102 or 1104 may have a 1:1 correspondence with acorresponding alignment channel and/or alignment rib of the interfacesleeve 1104. This may prevent the sample support 1102 or 1104 from beinginserted into the interface sleeve 1104 except when the sample support1102 or 1104 is rotated to align the alignment channels and alignmentribs.

In use, a tissue sample 1008 (not shown) can be placed on an uppersurface 1139 of the base 1113 of the bottom sample support 1106.Optionally, a layer of foam (not shown) may be positioned above the base1113, and the tissue sample 1008 may instead be placed on the layer offoam. Next, the bottom sample support 1106 can be positioned below theinterface sleeve 1104 and rotated to align the alignment ribs 1132 withthe alignment channels 1134, to align the alignment rib 1136 with thealignment channel 1138 and to align the latches 1110 with the notches1112, as shown in FIGS. 25A and 25B. Next, the gripping members 1120 ofthe bottom sample support 1106 can be moved radially inwardly againstthe bias of the resilient arms 1122 to retract the latches 1110, and thebottom sample support 1106 can be moved upwardly into the interfacesleeve 1104 through the lower end 1126 until an upper wall 1140 of thebottom sample support 1106 abuts a flange 1142 that extends inwardlyfrom the side walls 1124, as shown in FIG. 25C. As shown, the flange1142 is positioned to permit the bottom sample support 1106 to move intothe interface sleeve 1104 a sufficient distance for the latches 1110 toclear the notches 1112. Afterwards, the gripping members 1120 can bereleased to move the latches 1110 radially outwardly under the bias ofthe resilient arms 1122. Thus, the bottom sample support 1106 isconstrained inside the interface sleeve 1104. Longitudinal movement isconstrained by the flange 1142 and the notches 1112, and rotationallymovement is constrained by the alignment rib 1136 and the alignmentchannels 1134.

The top sample support 1102 interacts with the interface sleeve 1104 inan analogous manner as the bottom sample support 1106. The difference isthat, in the example shown, the top sample support 1102 is moved intothe interface sleeve 1104 through the upper end 1128, and can bepositioned at a select longitudinal position, between the upper andlower ends 1128 and 1126. The gripping members 1120 can be released tomove the latches 1110 radially outwardly under the bias of the resilientarms 1122 to between a pair of longitudinally spaced notches 1112. Thusthe longitudinal position of the top sample support 1102 is constrainedby the interaction of the latches 1110 between respective pairs oflongitudinally spaced notches 1112. In practice, the longitudinalposition of the top sample support 1102 may be selected to firmly hold atissue specimen (not shown) between the top and bottom sample supports1102 and 1106. As with the bottom sample support 1106, the top samplesupport 1102 can optionally include a foam layer that makes contact withthe tissue sample instead of the upper surface of the base 1113 of thetop sample support 1102.

In the example shown, the top and bottom sample supports 1102 and 1106may be disengaged from the interface sleeve 1104 by using the grippingmembers 1120 to retract the latches 1110, and then withdrawing thesample supports 1102 or 1104 from the interface sleeve 1104.

The container 1100 is shown including an optional bottom lid 1108. Thebottom lid 1108 as shown includes a circular base 1144 and a cylindricalside wall 1146 that depends from the base 1144. The side wall 1146 issized to receive the lower end 1126 of the interface sleeve 1104. Asshown, an interior of the side wall 1146 includes a plurality of guidechannels 1148 that extend downwardly from an upper end 1150. A lockingslot 1152 is shown extending tangentially from a bottom of each guidechannel 1148.

In the example shown, the interface sleeve 1104 includes a plurality oflocking pins 1154 extending radially outwardly from its side wall 1124proximate the lower end 1126. In use, the bottom lid 1108 is positionedbelow the interface sleeve 1104 and rotated to align a guide channel1148 with each locking pin 1154. The bottom lid 1108 can then be movedupwardly to receive the lower end 1126 of the interface sleeve 1104between its side walls 1146, and receive each locking pin 1154 in one ofits guide channels 1148. Next, the bottom lid 1108 can be rotated tomove each locking pin 1154 into a locking slot 1152, thereby releasablyengaging the bottom lid 1108 with the interface sleeve 1104.

In some cases, the bottom lid 1108 may prevent secretions (e.g. bodilyfluid or blood) from a contained tissue sample from dripping out of thecontainer 1100. In some embodiments, the container 1100 alternatively oradditionally includes a top lid (not shown) similar to the bottom lid1108 that releasably engages locking pins 1154 extending radiallyoutwardly from the side wall 1124 proximate the upper end 1128. A toplid may be engaged to help preserve the sterility of the container 1100prior to use, and/or to seal a specimen in the container 1000 duringtransportation and storage. In some embodiments, the top and bottom lids1108 may form a liquid and air tight seal with the interface sleeve1104. This may permit the interior volume 1130 containing a tissuesample (not shown) to be filled with a liquid preservative (e.g.formalin) to preserve the tissue sample. In some cases, one or both ofthe top sample support 1102 and the bottom sample support 1106 includesvent holes 1156 which may permit a liquid preservative to be poured intothe interior volume 1130 while the tissue sample is optionally heldfirmly in place. As described above, with respect to the vent holes 1056of container 1000, vent holes 1156 may also help to regulate humidityinside container 1100 (e.g. to prevent condensation from interferingwith a clear image capture). Still, in alternative embodiments, thecontainer 1100 may not include a top or bottom lid.

For example, one potential usage scenario of the container 1100 wouldinvolve placement of a specimen in the container 1100, scanning of thespecimen using OCT, transmission of the container 1100 (still containingthe specimen) to a radiology or MRI department, scanning of thecontainer 1100 using X-RAY or MRI while the specimen is still in thecontainer 1100, and then the submerging of the specimen in a preservingfluid such as formalin when the specimen is a tissue sample. The top andbottom lids 1108 of the container 1100 are sealable, so that thecontainer 1100 may allow the specimen to be held in place withinformalin until the specimen is later imaged, stored or otherwiseprocessed.

It should be noted that once the specimen in the container 1100 isscanned using another modality, in addition to OCT, the imaging datafrom the other modality could be co-registered with the OCT image datasince the specimen may be maintained in the same position whenundergoing two or more types of imaging that are different from oneanother due to the upper and lower surfaces of the container 1100 firmlyengaging the specimen in place, the interface sleeve being transparentfor these different imaging modalities and the orientation markers onone or more of the interface sleeve, the bottom sample support and thetop sample support can be imaged using the different imaging modalities(i.e. the orientation markers are opaque to these imaging modalities).The user could then view the two images on the same interface. Forexample, if the container 1100 underwent X-ray imaging after OCTimaging, a user could view the radiograph information alongside the OCTimage data in the same interface. Alternatively, if the other modalityis MRI Imaging, then hi resolution data for the surface of the MRI imagecould be obtained by overlaying the OCT image data on the MRI imagedata. It should be noted that this technique may be used with the otherembodiments of the containers described herein.

Reference is now made to FIGS. 26A and 26B. FIG. 26A shows a perspectiveview of a container 1200 in accordance with another embodiment. FIG. 26Bis a cross-sectional view taken along line 26B-26B in FIG. 26A. Thecontainer 1200 is substantially similar to container 1100, with someexceptions including that container 1200 has a cuboid shape, and its topand bottom sample supports 1202 have a different biasing mechanism forbiasing the latches 1204 outwardly beyond the side walls 1206.

FIG. 26C shows a perspective view of a surface of a top or bottom samplesupport 1202 that is opposite the surface that is adjacent to the tissuesample in use. In the example shown, the latches 1204 extend outwardlyfrom upstanding arms 1208 which are integrally formed with the sidewalls 1206. As shown, each arm 1208 is cantilevered at its base 1210 toresiliently bias the latches 1204 against radial movement to theposition shown. The sample support 1202 is also shown including fourintegrally formed arms 1212 which pivot about a central post 1214. Anupstanding cam 1216 is shown extending from a distal end of each arm1212 and being supported in position by a pair of ribs 1218. Also, agripping member 1220 is coupled to the integrally formed arms 1212.

FIG. 26D shows a perspective view of the container 1200 in a closedconfiguration. In use, a tissue sample (not shown) is placed on a base1221 of a bottom sample support 1202. As in the embodiments describedabove, a foam layer (not shown) may optionally be positioned on the base1221 and the tissue sample may be instead placed on the foam layer.Afterward, the top and bottom sample supports 1202 can be moved insidethe upper and lower ends 1223 and 1224 of the interface sleeve 1226. Thegripping member 1220 can then be grasped to rotate the arms 1212 aboutthe post 1214. As shown, when the arms 1212 are rotated clockwise, eachcam 1216 makes sliding contact with a radially inward protrusion 1228 onan upper end of each arm 1208. This contact urges each arm 1208 to bendradially outwardly, and in turn moves each latch 1204 radially outwardlyinto engagement with the notches (or teeth) 1222 of the interface sleeve1226.

In the example shown, each arm 1212 includes a locking slot 1230 that issized and positioned to mate with a corresponding locking pin 1232,which extends from a base 1221 of the sample support 1202. When the arms1212 are rotated about the post 1214 to urge the latches 1204 to moveradially outwardly, the locking slot 1230 receives the locking pin 1232to lock the rotary position of the arms 1212. To disengage the samplesupport 1202 from the interface sleeve 1226, a user can grasp thegripping member 1220 to rotate the arms 1212 counterclockwise separatingthe locking slot 1230 from the locking pin 1232, separating the cam 1216from the protrusion 1228, retracting the latches 1204 by the bias of thecantilevered arms 1208, and disengaging the latches 1204 from thenotches 1222. Afterward, the sample support 1202 can be withdrawn fromthe interface sleeve 1226.

FIG. 26E shows a perspective view of a sample support 1300 in accordancewith another embodiment. In some embodiments, the sample support 1300substitutes one or both of the top and bottom sample support 1202 of thecontainer 1200.

In the example shown, the sample support 1300 includes side walls 1302sized and shaped to be received inside the interface sleeve 1226. Theside walls 1302 as shown include a plurality of openings 1304, each ofwhich is sized to receive a corresponding latch 1306. As shown, eachlatch 1306 extends radially outwardly of a central biasing member 1308that biases the latches 1306 against radial movement to the positionshown.

The biasing member 1308 as exemplified includes a generally rectangularframe 1310 that is secured to a base 1312 by a plurality of pairs ofcatches 1314. As shown, each pair of catches 1314 holds an arm 1316 insliding contact with the base 1312. Further, each arm 1316 is shownconnecting a corresponding latch 1306 to the frame 1310. Thus, thecatches 1314 may restrict the movement of the arms 1316 and the latches1306 to the radial direction.

In the example shown, the sample support 1300 also includes arectangular cam 1318 that is rotatably mounted at its center to the base1312. A gripping member 1320 is shown connected to the cam 1318 tofacilitate hand-rotation of the cam 1318. In the example shown, thegripping member 1320 is formed by upstanding panels 1322 which extendfrom opposite sides of the cam 1318. As exemplified, the cam 1318 issized and shaped to make sliding contact with the frame 1310 whenrotated to bend the frame 1310 and urge the arms 1316 and the latches1306 to move radially outwardly. In use, the gripping member 1320 can bemanipulated to rotate the cam 1318 to urge the latches 1306 to moveradially outwardly and engage the notches (or teeth) 1222 of theinterface sleeve 1226. The gripping member 1320 can also be manipulatedto rotate the cam 1318 out of contact with the frame 1310 to withdrawthe latches 1306 under the bias of the frame 1310 and disengage thenotches (or teeth) 1222.

While the applicant's teachings described herein are in conjunction withvarious embodiments for illustrative purposes, it is not intended thatthe applicant's teachings be limited to such embodiments. On thecontrary, the applicant's teachings described and illustrated hereinencompass various alternatives, modifications, and equivalents, withoutgenerally departing from the embodiments described herein.

The invention claimed is:
 1. A method for generating a wide fieldoptical coherence tomography (OCT) image of a portion of a sample,wherein the method comprises: creating a surface map of the sample;acquiring raw OCT data of the portion of the sample based on the surfacemap by identifying an initial scan position and a final scan position onthe surface map, the initial scan position corresponding to a highestpoint of the surface of the portion of the sample and the final scanposition corresponding to a lowest point of the surface of the portionof the sample, and for a given set of vertical OCT images, acquiring theraw OCT data to generate at least a first OCT image with an upper edgeof an imaging window aligned with the initial scan position and a secondOCT image with an upper edge of an imaging window aligned with the finalscan position; generating a plurality of OCT images from the raw OCTdata; and combining two or more OCT images of the plurality of OCTimages to create the wide field OCT image.
 2. The method of claim 1,wherein acquiring the raw OCT data further comprises: operating ascanning head to capture the raw OCT data; and adjusting a position ofthe scanning head with respect to a surface of the sample based on atleast one of the surface map and OCT imaging parameters for the widefield OCT image.
 3. The method of claim 2, further comprising adjustingthe position of the scanning head from a first position to a secondposition that is different from the first position to accommodateunevenness at a surface of the portion of the sample.
 4. The method ofclaim 1, wherein the focus used to acquire the raw OCT data used togenerate the OCT images is substantially constant.
 5. The method ofclaim 1, wherein the method further comprises detecting a surfaceposition for each OCT image.
 6. The method of claim 5, wherein detectingthe surface position for a given OCT image comprises: identifying asignal portion associated with the given OCT image having a highestintensity value; and associating a depth position corresponding to thesignal portion having the highest intensity value as the surfaceposition.
 7. The method of claim 1, wherein combining two or more OCTimages comprises: aligning the first OCT image with a neighbouring OCTimage; and overlaying a portion of the first OCT image over a portion ofthe neighbouring OCT image to create a composite OCT image.
 8. Themethod of claim 7, wherein at least one of the first OCT image and theneighbouring OCT image is a composite OCT image resulting from thecombination of at least two other OCT images.
 9. The method of claim 7,wherein the neighbouring OCT image corresponds to a first section of asurface of the sample and the first OCT image corresponds to a secondsection of the surface of the sample that is different from the firstsection.
 10. The method of claim 9, wherein the second section and thefirst section are from a horizontal image set obtained from the sample.11. The method of claim 7, wherein the neighbouring OCT image and thefirst OCT image correspond to the same section of a surface of thesample and different depth positions of the sample.
 12. The method ofclaim 7, wherein combining two or more OCT images further comprises:creating a plurality of intermediary composite images by overlaying aportion of the first OCT image over a portion of the neighbouring image,wherein each intermediary composite image includes a different amount ofoverlap between the OCT image and the neighbouring image; determining anentropy associated with each intermediary composite image; and selectingthe intermediary composite image associated with a lowest entropy valueas the composite OCT image for the first OCT image and the neighbouringOCT image and recording the amount of overlap associated with theselected intermediary composite image.
 13. The method of claim 12,wherein combining two or more OCT images further comprises: identifyinga focused portion in each of the first OCT image and the neighbouringOCT image based on the respective surface positions; overlaying thefirst OCT image and the neighbouring OCT image based on the recordedamount of overlap; and retaining, in the composite OCT image, only thefocused portions from each of the first OCT image and the neighbouringOCT image.
 14. The method of claim 1, wherein acquiring the raw OCT datafurther comprises minimizing saturation effects in at least one OCTimage of the plurality of OCT images by: detecting a noise signal at aregion proximate to an external surface of the sample in the at leastone OCT image; determining whether an intensity of the noise signalexceeds an intensity threshold; and adjusting characteristics of the atleast one OCT image to reduce the intensity of the noise signal when theintensity of the noise signal exceeds the intensity threshold.
 15. Asystem for generating a wide field optical coherence tomography (OCT)image of a portion of a sample, wherein the system comprises: an inputport for receiving raw OCT data; an image processing module beingconfigured to process the raw OCT data to generate wide field OCT imagesby creating a surface map of the sample; acquiring raw OCT data of theportion of the sample based on the surface map by identifying an initialscan position and a final scan position on the surface map, the initialscan position corresponding to a highest point of the surface of theportion of the sample and the final scan position corresponding to alowest point of the surface of the portion of the sample, and for agiven set of vertical OCT images, acquiring the raw OCT data to generateat least a first OCT image with an upper edge of an imaging windowaligned with the initial scan position and a second OCT image with anupper edge of an imaging window aligned with the final scan position;generating a plurality of OCT images from the raw OCT data; andcombining two or more OCT images of the plurality of OCT images tocreate the wide field OCT image; and an output port to provide thewide-field OCT images to one of a user, a storage device and anothercomputing device.
 16. The system of claim 15, wherein the imageprocessing module is configured to acquire the raw OCT data by:operating a scanning head to capture the raw OCT data; adjusting aposition of the scanning head with respect to a surface of the samplebased on at least one of the surface map and OCT imaging parameters forthe wide field OCT image; and adjusting the position of the scanninghead from a first position to a second position that is different fromthe first position to accommodate unevenness at a surface of the portionof the sample.
 17. The system of claim 15, wherein the image processingmodule is configured to maintain a substantially constant focus toacquire the raw OCT data used to generate the OCT images.
 18. The systemof claim 15, wherein the image processing module is configured to detecta surface position for each OCT image by: identifying a signal portionassociated with the given OCT image having a highest intensity value;and associating a depth position corresponding to the signal portionhaving the highest intensity value as the surface position.
 19. Thesystem of claim 15, wherein the image processing module is configured tocombine two or more OCT images by: aligning the first OCT image with aneighbouring OCT image; and overlaying a portion of the first OCT imageover a portion of the neighbouring OCT image to create a composite OCTimage.
 20. The system of claim 19, wherein at least one of the first OCTimage and the neighbouring OCT image is a composite OCT image resultingfrom the combination of at least two other OCT images.
 21. The system ofclaim 19, wherein the neighbouring OCT image corresponds to a firstsection of a surface of the sample and the first OCT image correspondsto a second section of the surface of the sample that is different fromthe first section, and wherein the second section and the first sectionare from a horizontal image set obtained from the sample.
 22. The systemof claim 19, wherein the neighbouring OCT image and the first OCT imagecorrespond to the same section of a surface of the sample and differentdepth positions of the sample.
 23. The system of claim 19, wherein theimage processing module is configured to combine two or more OCT imagesfurther by: creating a plurality of intermediary composite images byoverlaying a portion of the first OCT image over a portion of theneighbouring image, wherein each intermediary composite image includes adifferent amount of overlap between the OCT image and the neighbouringimage; determining an entropy associated with each intermediarycomposite image; and selecting the intermediary composite imageassociated with a lowest entropy value as the composite OCT image forthe first OCT image and the neighbouring OCT image and recording theamount of overlap associated with the selected intermediary compositeimage.
 24. The system of claim 23, wherein the image processing moduleis further configured to combine the two or more OCT images by:identifying a focused portion in each of the first OCT image and theneighbouring OCT image based on the respective surface positions;overlaying the first OCT image and the neighbouring OCT image based onthe recorded amount of overlap; and retaining, in the composite OCTimage, only the focused portions from each of the first OCT image andthe neighbouring OCT image.
 25. The system of claim 15, wherein theimage processing module is configured to acquire the raw OCT datafurther by minimizing saturation effects in at least one OCT image ofthe plurality of OCT images by: detecting a noise signal at a regionproximate to an external surface of the sample in the at least one OCTimage; determining whether an intensity of the noise signal exceeds anintensity threshold; and adjusting characteristics of the at least oneOCT image to reduce the intensity of the noise signal when the intensityof the noise signal exceeds the intensity threshold.
 26. Anon-transitory computer-readable medium storing computer-executableinstructions, the instructions for causing an image processing module toperform a method for generating a wide field optical coherencetomography (OCT) image of a portion of a sample, wherein the methodcomprises: creating a surface map of the sample; acquiring raw OCT dataof the portion of the sample based on the surface map by identifying aninitial scan position and a final scan position on the surface map, theinitial scan position corresponding to a highest point of the surface ofthe portion of the sample and the final scan position corresponding to alowest point of the surface of the portion of the sample and for a givenset of vertical OCT images, acquiring the raw OCT data to generate atleast a first OCT image with an upper edge of an imaging window alignedwith the initial scan position and a second OCT image with an upper edgeof an imaging window aligned with the final scan position; generating aplurality of OCT images from the raw OCT data; and combining two or moreOCT images of the plurality of OCT images to create the wide field OCTimage.
 27. A method for generating a wide field optical coherencetomography (OCT) image of a portion of a sample, wherein the methodcomprises: receiving a set of OCT images corresponding to the portion ofthe sample, the set of OCT images including a first OCT image and aneighbouring OCT image; aligning the first OCT image with theneighbouring OCT image; and overlaying a portion of the first OCT imageover a portion of the neighbouring OCT image to create the wide fieldOCT image by (1) creating a plurality of intermediary composite imagesbased on the first OCT image and the neighbouring image, eachintermediary composite image having a different amount of overlapbetween the OCT image and the neighbouring image, (2) determining anentropy associated with the overlap in each intermediary compositeimage; and (3) selecting the intermediary composite image associatedwith a lowest entropy value as the wide field OCT image for the firstOCT image and the neighbouring OCT image and recording the overlap. 28.The method of claim 27, wherein the neighbouring OCT image correspondsto a first section of a surface of the sample and the first OCT imagecorresponds to a second section of the surface of the sample that isdifferent from the first section, the second section and the firstsection are from a horizontal image set obtained from the sample and themethod comprises using horizontal stitching to create the wide field OCTimage.
 29. The method of claim 27, wherein the neighbouring OCT imageand the first OCT image correspond to the same section of a surface ofthe sample and different depth positions of the sample and the methodcomprises using vertical stitching to create the wide field OCT image.30. The method of claim 27, wherein overlaying the portion of the firstOCT image over the portion of the neighbouring OCT image furthercomprises: identifying a focused portion in each of the first OCT imageand the neighbouring OCT image based on respective surface positions;overlaying the first OCT image and the neighbouring OCT image based onthe recorded overlap to generate the wide field OCT image; andretaining, in the wide field OCT image, only the focused portions fromeach of the first OCT image and the neighbouring OCT image.
 31. A methodfor generating a wide field optical coherence tomography (OCT) image ofa portion of a sample, wherein the method comprises: creating a surfacemap of the sample; acquiring raw OCT data of the portion of the samplebased on the surface map; generating a plurality of OCT images from theraw OCT data; combining two or more OCT images of the plurality of OCTimages to create the wide field OCT image by: aligning a first OCT imagewith a neighbouring OCT image; creating a plurality of intermediarycomposite images by overlaying a portion of the first OCT image over aportion of the neighbouring image, wherein each intermediary compositeimage includes a different amount of overlap between the OCT image andthe neighbouring image; determining an entropy associated with eachintermediary composite image; and selecting the intermediary compositeimage associated with a lowest entropy value as the composite OCT imagefor the first OCT image and the neighbouring OCT image and recording theamount of overlap associated with the selected intermediary compositeimage.
 32. A system for generating a wide field optical coherencetomography (OCT) image of a portion of a sample, wherein the systemcomprises: an input port for receiving raw OCT data; an image processingmodule being configured to process the raw OCT data to generate widefield OCT images by creating a surface map of the sample; acquire rawOCT data of the portion of the sample based on the surface map; generatea plurality of OCT images from the raw OCT data; and combine two or moreOCT images of the plurality of OCT images to create the wide field OCTimage by: aligning the first OCT image with a neighbouring OCT image;overlaying a portion of the first OCT image over a portion of theneighbouring OCT image to create a composite OCT image; creating aplurality of intermediary composite images by overlaying a portion ofthe first OCT image over a portion of the neighbouring image, whereineach intermediary composite image includes a different amount of overlapbetween the OCT image and the neighbouring image; determining an entropyassociated with each intermediary composite image; and selecting theintermediary composite image associated with a lowest entropy value asthe composite OCT image for the first OCT image and the neighbouring OCTimage and recording the amount of overlap associated with the selectedintermediary composite image; and an output port to provide thewide-field OCT images to one of a user, a storage device and anothercomputing device.